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Computers and Electronics in Agriculture 76 (2011) 96104

Contents lists available at ScienceDirect

Computers and Electronics in Agriculture

journa l homepage: www.e lsev ier .com/ locate /compag

riginal papers

coustic monitoring system to quantify ingestive behavior of free-grazing cattle

illiam M. Claphama,, James M. Feddersa, Kim Beemanb, James P.S. Neela

USDA-ARS, Appalachian Farming Systems Research Center, 1224 Airport Rd, Beaver, WV 25813, USAEngineering Design, 262 Grizzly Peak Blvd, Berkeley, CA 94708, USA

r t i c l e i n f o

rticle history:eceived 6 May 2010eceived in revised form3 December 2010ccepted 14 January 2011

eywords:coustic monitoringntakeorageattle

a b s t r a c t

Estimating forage intake by free-grazing livestock is difficult and expensive. Previous approaches includebehavioral observation, ratio techniques using indigestible markers, mechanical recording of ingestivejaw motion, and acoustic recording of ingestive behaviors. Acoustic recording shows great potential buthas been limited by the difficulty and time required tomanually identify and classify ingestive events.Wepresent an acoustic recording and analysis system that automatically detects, classifies, and quantifiesingestive events in free-grazing beef cattle. The system utilizes a wide-frequency acoustic microphoneclose to the animals mouth, mathematical signal analysis to detect and measure ingestive events, andstreaming data analysis capable of handling an unlimited amount of data. Analysis parameters can bereconfigured for different animals, forages and other changing conditions. The system measures theacoustic parameters of ingestive events, such as duration, amplitude, spectrum and energy, which canrazing behavior support further event classification and become the inputs to a forage intake model. We validated ourdetection and classification technique against the results of trained human observers based on fieldstudies with grazing steer. The software detected 95% of manually identified bites in an event-by-eventcomparison. Field observations and sound attenuation analysis indicate that sounds from adjacent live-stock and ambient pastoral environments have an insignificant effect upon the integrity of the recordedacoustic data set. We conclude that wideband acoustic analysis allows us to identify ingestive eventsaccurately and automatically over extended periods of time.. Introduction

Soundmetrics, including frequency and amplitude, can be usedo classify and quantify food and ingestive processes. Acoustic anal-sis was used to quantify texture (crispness/crunchiness) in food.iu and Tan (1999) studied snack food crispness and demonstratedhat sound features corresponded (R2 =0.89)with a trained sensoryanel, concluding that sound signal analysis provided an effec-ive measure of crispness. Similar data were collected measuringpple and potato crispness (Zdunek and Bednarczyk, 2006). Acous-ic envelope detectors were developed (e.g. Stable Micro Systems,urrey, UK) to quantify the crispness and sensory qualities of bis-uits and other fresh and processed foods.

Forage intake by grazing livestock is one of the keys tonderstanding forage grazing system dynamics (Ungar, 1996).

owever, estimating intake of free-ranging livestock is difficult andxpensive. Technology and improved methods have significantlymproved our ability to collect grazing behavior data. Procedureso estimate intake include indirect methods such as ratio or index

Corresponding author. Tel.: +1 304 256 2857; fax: +1 304 256 2921.E-mail address: William.Clapham@ars.usda.gov (W.M. Clapham)

168-1699/$ see front matter. Published by Elsevier B.V.oi:10.1016/j.compag.2011.01.009Published by Elsevier B.V.

techniques, where intake is calculated via measures of digestibility(Cordova et al., 1978), and direct methods such as direct behav-ioral observation; mechanical recording of chews, bites, and jawactivity using jaw sensors (Chambers et al., 1981; Champion et al.,1998); acoustic recordings in combination with video recordingsor direct observation (Griffiths et al., 2006; Laca et al., 1992). Thedevelopment of jaw sensors and small data recorders (Rutter etal., 1997) provided a wealth of data regarding ingestive behavior,particularly because software to classify the data was developedto quantify jaw movement events (Rutter, 1998). However, esti-matesof intake require calibrationof the relationshipsbetweenbitecount and forage ingested andmodeling variation in bite size. Somesuccess was achieved by combining video and acoustic recordingsof ingestive behavior combined with short-term studies of massdifference from 0.14m2 field-grown, sods placed in metal trays(Laca and WallisDeVries, 2000). Acoustic methods pioneered byLaca et al. (1992, 1994), and used by Galli et al. (2006) and Ungarand Rutter (2006) utilized an inward-facingmicrophonemounted

on the forehead of the animal to record the sounds of bites andchews. Ungar and Rutter (2006) demonstrated that data collectedusing an inward-facingmicrophone corresponded to data collectedusing the IGERBehaviourRecorder in10-mingrazing sessionsusingsix cattle. Although acoustic methods demonstrate great promise

dx.doi.org/10.1016/j.compag.2011.01.009http://www.sciencedirect.com/science/journal/01681699http://www.elsevier.com/locate/compagmailto:William.Clapham@ars.usda.govdx.doi.org/10.1016/j.compag.2011.01.009

W.M. Clapham et al. / Computers and Electronics in Agriculture 76 (2011) 96104 97

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ig. 1. Photograph of a heifer wearing a halter with attached digital recorder andicrophone.

or recording and quantifying ingestive events, manual classifica-ion of these events is difficult and time consuming and in need ofutomation (Ungar and Rutter, 2006). Milone et al. (2009) createdoftware that used hiddenMarkovmodels to automate the identifi-ation and classification of ingestive events in sheep and classifiedites and chews with an accuracy of 58 and 89%, respectively.In this paper, we describe the development of a digital audio

ecording and automated event classification system that recordsrazing sounds, detects bite events and compiles grazing event databite number and acoustic event parameters). The objectives of thiseport are to: (1) describe the hardware and software componentsnd processing steps; (2) compare the spectral characteristics ofngestive events recorded over wide and narrow frequency ranges,o demonstrate the need forwide-frequency acoustic data for accu-ate automated detection of bite events; (3) establish the acousticeatures required to differentiate and classify ingestive events; (4)ocument the amount of acoustic cross contamination from ani-als grazing nearby; and (5) use manual analysis of audiovideo

ecordings to validate the ability of the automated system to detectnd classify bite events.

. Methods and materials

.1. Field conditions

Ingestive behavior was investigated at West Virginia Univer-ity Willow Bend Farm near Union, WV, USA (37.547N latitude,0.528W longitude). Halter-trained, 1618 month old, angus-ross steers or heifers (450550kg live weight) were used duringhe experiments. The free-ranging animals were maintained onixed, perennial pasture consisting primarily of tall fescue (Fes-

uca arundinacea Schreb.), orchardgrass (Dactylis glomerata L.),luegrass (Poa pratensis L.) and white clover (Trifolium repens L.).ecording sessions were conducted between the hours of 8:00AMnd 1:00PM local time between July and October over five years.

uring a recording session, the animals were given access to eitherixed perennial pasture, alfalfaorchardgrass pasture or a puretandof triticale (XTriticosecaleWittmack) (amixtureof Trical 2700nd Trical 336; Resource Seeds Inc. P.O. Box 1319, Gilroy, CA 95021)hat had been established in early August.ophone. Insets show details of: (a) recorder in the protective plastic case and (b)

2.2. Hardware components and setup

The recording system (Fig. 1) was designed to have minimalintrusion on the behavior of the livestock. The system consisted of adigital recorder (Edirol R-09 24-bit recorder, ProgramVersion 1.20,RolandCorporationUS, 5100S. EasternAve., LosAngeles, CA90040-2938) and omni-directional lavalier microphone (Sennheiser ME2-US, Sennheiser Electronic GmbH & Co. KG, 30900 Wedemark,Germany) mounted on a 1-inch nylon cow halter (Weaver Leather,7540 CR 201, PO Box 68, Mt. Hope, OH 44660). The recorder wasplaced inside a water resistant plastic enclosure (Pelican 1020MicroCase, PelicanProducts, Inc., 23215EarlyAvenue, Torrance, CA90505) and bolted onto the back strap of the halter to ride behindthe head of the animal. The microphone was attached to the frontstrap of the halter 5 cm from the right corner of the animalsmouth.Four-inchwideVetrap tape (3MAnimal Care Products, St. Paul,MN55144-1000) was used to secure the microphone and microphonecable to the halter.

Sound data was recorded onto a 4GB SDmemory card (SandiskExtreme III SDHC Card Sandisk Corporation, 601 McCarthy Blvd.,Milpitas, CA 95035) in the Edirol R-09. All recordings were madeat 44.1 kHz sampling rate and 16-bit resolution, providing a nom-inal 22kHz recording bandwidth and 96dB dynamic range, andstored in the WAV (Waveform Audio) file format. Recorded soundfiles contain the voltage output from themicrophone, representingthe time-varying acoustic pressure at the microphone diaphragm.Voltage values can be converted to numerical sound pressure byapplying a calibration factor incorporating microphone transducergain (VPa1) and amplifier gain. Prior to each recording session,the recorder input level, sampling rate and bit resolution were set;the recorder was secured inside the plastic enclosure; and the hal-ter was secured on the animal. Four to six animals grazed togetherduringeach recording session inpaddocks thatwereapproximately0.1ha in size.2.3. Sound file processing and analysis

Files from each recording session were uploaded onto aDell Optiplex 745 personal computer (Dell Inc., One DellWay, Round Rock, TX 78682, USA) (3.40GHz Intel Pentium D

98 W.M. Clapham et al. / Computers and Electronics in Agriculture 76 (2011) 96104

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selected based on the differing amplitude and frequency character-istics of bite vs. non-bite events. Detection valueswere then refinedthrough trial and error by comparing automated and manual bitecounts.

Table 1Parameter settings used to detect bites from acoustic recordings of grazing sessionswith SIGNAL software.

Parameter Value

Low frequency cutoff 17kHzHigh frequency cutoff NoneEnvelope decay time 15msDetection threshold 0.013VMinimum event gap 250msig. 2. Schematic of testing and validation procedure for automated processing ofteer 751 while grazing mixed pasture on July 28, 2005. Rectangular boxes on the wre shown in the box below the waveform, as an example of program output.

PU; 4 GB RAM; Microsoft Windows XP Professional, version.1.2600). Audacity software for Windows (version 1.3.5 beta,ttp://audacity.sourceforge.net/)wasused toprepare the rawWAVles for analysis. The stereo files created by the R-09 recorder wereeduced to monaural files by extracting one channel. A high-passlter (rolloff = 24dB, filter quality =0.1, cutoff frequency=600Hz)as applied to reduce wind sounds and other low frequency noise.

n future work, we will attempt to eliminate or reduce the need forhis filter by improving microphone wind-resistance.

Identification, enumeration and measurement of bite eventsn the pre-processed files were performed using the SIGNALound analysis program for Windows (version 5.00.28, Engi-eering Design, 262 Grizzly Peak Blvd, Berkeley, CA 94708,SA, www.engdes.com). SIGNAL analyzed each sound file andutomatically detected and measured bite events, recording theeasurement data into a log file.The SIGNAL software processed the monaural WAV files at

pproximately 10 times realtime, i.e., analyzing 10min of acous-

ic data per minute. The software operates in a two-step processFig. 2). First an event is detected, then event parameters areeasured and recorded in a log file. This process is performed

epeatedly, from the beginning of the file to the end with the goalf detecting every target event in the file. SIGNAL detects eventsrecordings. The waveform represents acoustic pressure over 10 s, recorded fromrmmark bite events detected by the SIGNAL program. Measured event parameters

based on sound characteristics such as frequency, intensity, dura-tion and time between events (Table 1). The values we chose forthese parameters collectively define a bite event to the softwareand enable it to detect bite events. Initial detection values wereMinimum pulse length 1msMinimum event length 100msMaximum event length 1000msPre-event time extension 100msPost-event time extension 100ms

http://audacity.sourceforge.net/http://www.engdes.com/

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Fig. 3. Spectral density plots of: (a) wideband (020kHz) recordings and (b) nar-rowband (08kHz) recordings of one bite and one chew event of a heifer grazingvegetative triticale. Spectral energy below 600Hz was removed from all signals bya high-pass filter during preprocessing. Spectra were derived from a 16,384-pointFHct

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ourier transform, adjusted for 1Hz spectral bandwidth and smoothedwith a 1000-z rectangular window. Co-plotted spectra are normalized for equal RMS power toontrast spectral distribution. Brackets indicate the 1722kHz frequency band usedo detect bite events.

Key distinguishing characteristics of bite events, relative tohewing or other sounds, are the high frequencies produced by thenitial shearing or ripping of forage. Therefore we programmed theetection software to evaluate only energy at frequencies between7kHz and the upper recording limit of 22kHz (see Table 1), ashown in Fig. 3. For purposes of bite detection, we programmed theoftware to register an event start time (Ts) when event amplituden the1722kHz rangeexceededagivendetection thresholdandanvent end time (Te) when amplitude subsequently dropped belowhis threshold. Events were discarded as spurious if, for example,heir durations were shorter than the minimum designated eventength or longer than the maximum designated event length.

Detailed examination of time-domain representations of bitesndicates a low level of bite energy immediately prior to Ts as soundnergy increases frombackground levels to the detection thresholdnd immediately after Te as bite energy dissipates and drops belowhe threshold. We programmed SIGNAL to include this energy bysing an event measurement period of Ts 100ms to Te + 100mss indicated by the pre-event and post-event time extensions inable 1. We expect our technique will detect and extract theite portion of chewbite events (Laca and WallisDeVries, 2000)onsisting of a chew followed immediately by a bite. We expectontamination due to spurious inclusion of the chew segment wille small because: (1) the 600Hz high pass filter utilized prioro event detection removes much of the chew energy and (2)he 100ms time extension matches the small separation depictedetween the chew and bite segments of the chewbite event illus-rated in Laca and WallisDeVries (2000).Our approach to bite detection is not intended to measure biteuration precisely but rather to detect automatically the occur-ence of bite events with high reliability, count bite events andeasure the soundenergyproducedwhen forage is sheared in eachite. For example, our approach to bite detection does not includenics in Agriculture 76 (2011) 96104 99

the time taken by the animal to gather forage with the tongue andbring it into the mouth prior to shearing, a process that will varywith sward structure and composition.

This phase of our work did not require calculating the abso-lute energy of acoustic events. However, we did compare relativeacoustic energy levels, for example, to estimate thepercentage con-tamination of bite sounds by adjacent animals (Section 2.5.1). Forthis purpose,wecalculated the total energyfluxof anevent, definedas

Jm2 s1 dt. Since instantaneous energy flux in a plane acoustic

wave is p2/0c, where p is pressure and 0 and c are, respec-tively, the density and propagation velocity of the medium, totalenergy flux depends on

p2 dt. Since the amplitude of our acoustic

data is proportional to pressure, our program calculated the time-integrated squared amplitude for each bite as ameasure of relativeevent energy.

2.4. Comparison of wideband and narrowband recordings ofingestive sounds

Our technique for automatingdetectionandclassificationof biteevents distinguishes bites from chews based on high-frequency(17kHz and above) characteristics and therefore requires full band-width acoustic recordings. To confirm this, we made wideband(022kHz) and narrowband (08kHz) recordings of the sameingestive events. We use these terms to refer to these band-widths throughout this paper. Wideband recordings were madewith a halter-mounted ME 2 acoustic microphone attached nearthe animals mouth and narrowband recordings were made witha forehead-mounted piezoelectric microphone fashioned from a2.5 cm diameter piezoelectric transducer (Edmunds Scientifics,Tonawanda,NY14150,USA). Signalswere recorded simultaneouslyon separate channels using the stereo capability of the Edirol R-09.

This system was mounted on one heifer grazing triticaleon October 20, 2009 and on another heifer grazing triticaleon October 22, 2009. Five bite events and five chew eventswere randomly selected from wideband data and similarly fromnarrowband data for a total of 20 exemplars animal1, toavoid crowding on the principal components plot. Two tem-porally synchronized monaural files, one wideband and onenarrowband, were created from the stereo file for each eventusing Audacity software. The frequency spectra of the 40 files(2 animals10events animal1 2files event1) were analyzedbySIGNALusingFourier transformtechniques. Foreachfile, relativespectral amplitude in dBwasdetermined at 86.1Hz intervals acrossthe spectral range from 0 to 22000Hz. These 256 values for each ofthe 40 events were then subjected to principal component analysisusing the PRINCOMP procedure of SAS for Windows, version 9.2(SAS Institute, Cary, North Carolina 27513, USA).

2.5. Estimating acoustic contamination of recordings

Our recordings of acoustic bite events can be contaminated intwo ways: by bite sounds from other animals and by non-targetnoise events such as insects or jet plane flyovers.

2.5.1. Cross-contamination from other bite soundsOur studies involved multiple animals grazing together in close

proximity, which creates the possibility that a recorded bite fromone animal (the target) may include bite sounds from nearby(non-target) animals.We call this bite-sound cross-contamination.

Significant cross-contamination can degrade the automated detec-tion process with false triggers, as well as corrupt quantitativemeasurements of detected bite events, and has been noted asa serious concern (Ungar and Rutter, 2006). We quantify cross-contamination as the fraction of recorded target bites that contain

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ignificant energy from the bite sounds of non-target animals,efined as a contaminating energy level of 1% or greater relativeo the target bite sound energy, as measured at the target micro-hone. The 1% contamination level was selected as the thresholdelow which contamination would have minimal impact on bitenergy measurements.We could not directly measure bite-sound cross-contamination

nourfield recorded sound signals as a functionof animal proximityue to thedifficulty of obtaining time-correlated acoustic andprox-mity data without suitably tame and trained animals. Instead weodeled cross-contamination in the following way. We observed

nter-animal separationdistances under field conditions,measuredhe average rate of bite production in field recordings, and appliedhe physics of sound attenuation in air to calculate contaminatingite energy at varying distances. The equation for acoustic radia-ion in free space states that energy attenuates in proportion tohe squared distance from the sound source. Assuming target andon-target bites have similar source energy, the sound energy ofhe non-target bite will exceed 1% of target bite energy when theon-target animal is less than 10 times as far from the record-ng microphone as the target animals mouth. Since the recordingicrophone is mounted 5 cm from the targets mouth, we are con-erned with animal encounters closer than 50 cm, in which theontaminating acoustic energywould be 1% (5 cm/50 cm)2 ormoref the target energy.We observed six heifers while they grazed together within a

6m34m paddock of vegetative triticale at 20-s intervals overve periods of 510min each. At each interval, we counted theumber of animals whose heads were within 1m of each other, ashis separation distance was easier to estimate in the field than the.5m critical distance. The number of interactions at 0.5m or lesss estimated by a linear interpolation between 0 and 1m.

.5.2. Contamination from non-target soundsNon-target noise events include intermittent sounds, such as

ies, birds, animal vocalizations, aircraft, farm equipment and roadraffic, and continuous sounds, such as crickets and grasshoppers.s with cross-contamination, our goal was to estimate the contam-nation ofmeasured acoustic energy in the target bite. Intermittentounds such as aircraft flyovers are short-duration, potentiallyigh-intensity, and usually infrequent. We estimated the statisticsf this contamination in termsof the fractionof recordedbiteeventshat would be affected. Continuous sounds such as insects areong duration and low intensity. For example, large populations ofrickets in our pastures during mid to late summer create continu-us background sounds that are present in every event in the dataet. For these we estimated the ratio of target to non-target acous-ic energy and from this ratio we calculated the spurious increasen target energy as a percentage error. We analyzed two represen-ative 10-s sound samples, each containing a bite sequence withackgroundcricket soundsanda segmentof cricket soundswithoutites. Data was high-pass filtered at 600Hz to remove wind noisend other low-frequency ambient sounds. We calculated the ratiof bite energy (energy of bite with contaminating cricket soundsinus energy of cricket-only sounds) to cricket energy for eachample segment.

.6. Calibration and validation of automated bite detection

Audio/video recordingsof grazingactivitywereused to calibratendvalidatebitedetectionparametersusedby theSIGNALsoftware

Fig. 2). Digital camcorders were used to record ingestive behaviorf three animal subjects. Two animals (steers 751 and 527) wereecorded grazing mixed perennial pasture on July 28, 2005 andne animal (steer 710) was recorded grazing alfalfa on Septem-er 8, 2005. Continuous 1530min recordings of each animal onnics in Agriculture 76 (2011) 96104

eachdateweremadewith CanonElura 85Digital Camcorders usingMaxell Mini DV Digital Video Cassette tapes. Audio was transmit-ted from the halter-mounted acoustic microphone to a camcorderusing a Samson AL1 UHF transmittermounted on the halter behindthe neck of the animal. Camera operators were stationed outsideof the paddocks where the animals grazed.Whenmultiple animalswere recorded simultaneously on July 28, 2005, the transmitterswere set to different frequencies to isolate transmission of audiofrom each animal to separate cameras.

The single audio/video recording fromeach animal anddatewasdivided into 15min segments for analysis and converted to MOVfiles using iMovie software (AppleComputer, Cupertino, CA95014).Wecreated threefiles representing15minofdata for steer751, fourfiles representing 20min of data for steer 527, and five files repre-senting 15min of data for steer 710. The number of bites recordedon each MOV file was manually tallied by a trained observer whilereviewing the combined audio and video tracks. Manual classifi-cation was based on synchronized CD-quality audio and close-upvideo that provided visual details of the distinctivemouth andheadmovements associatedwithbite events. To estimate the accuracyofour counts, we repeated them using a second trained observer. Theaudio trackwasextracted fromeachMOVfileusing iMovie to createdigital audio WAV files (44.1Hz, 16-bit, monaural) for automatedbite analysis using the SIGNAL software.

For each animal and date, oneWAVfilewas chosen at randomasa calibration file for the SIGNAL program. SIGNAL detection param-eters (Table 1) were adjusted until the SIGNAL-derived bite countwas within 2% of the manual bite count for the calibration file.Generally, calibration involved minor adjustment to the detectionthreshold level among animals grazing the same forage type andlarger adjustments to the threshold between forage types. Otherdetection parameters generally did not change. Once calibrated,the SIGNAL program was then used to count the bites from theremainingfiles for that animal anddatewithout any further param-eter adjustments. In thismanner, SIGNAL-derived bite counts weredetermined for all of the WAV files.

Automated detection was validated against the manual base-line in two ways. First, automated and manual bite counts werecompared. The SAS GLM procedure was used to test for significantdifferences between the manual and SIGNAL-derived bite counts.Themodelwasa repeated-measuresANOVAwithbetweensubjectsfactors. Differences between the two count methods were evalu-ated by assessing differences in bite count within recordings andinteractionwasevaluated toassess anydifferences in countmethodamong the animals. We also calculated the standard deviation ofthe residual error of the automated bite counts compared to themanual bite counts.

Second, automated and manual bite sequences were comparedevent by event. One 5-minWAVfilewas selected at random for thisdetailed analysis. Using the audio and video recording, a trainedobserver recorded themid-bite time coordinate of every bite eventin the file (blind to the automated result on that file). Manual andautomated bites were then compared one by one. Bites were con-sideredmatched if themanually derived bite time fell between thestart and end times of an automatically detected bite. This analysisproduced three counts: matched bites, false positives (a non-bitesound detected as a bite by the automated system) and false nega-tives (a manually identified bite missed by the automated system).

3. ResultsThe self-contained, halter-mounted recording system waslightweight and did not appear to restrict animal activity. Directobservation suggested that the animals exhibited normal grazingbehavior while wearing the halters. The animals were typically

W.M. Clapham et al. / Computers and Electronics in Agriculture 76 (2011) 96104 101

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3.3. Calibration and validation of automated bite detection

Automated bite detection was validated through: (1) compar-ison of manual and automated bite counts on multiple data files

Table 2Summary of non-target noise sources.

Noise source Detected asspurious event

Broadbandenergy relative tobite events

Frequency ofoccurrence relativeto bite events

Crickets No Low High insummer/fall

Flies No Low Low in summer/fallCattle

vocalizationsNo High Lowig. 4. Wideband acoustic recordings of: (a) the bite event and (b) the chew event snit peak amplitude.

ager to graze fresh forage during the experimental trials afterpending the previous night penned with a limited amount oforage and/or dry hay. Laboratory tests showed that the 4GB SDemory cards could hold up to 6h and 24m of sound recordings,

onger than any of the trials conducted thus far. A pair of fullyecharged batteries powered the recorders long enough to fill theD cards in laboratory tests.Sound signal data can be expressed as sound intensity vs. time

Fig. 4) or as sound intensity vs. frequency for a given time periodFig. 3). Both representations provide insight into the recordedounds. A typical bite generated sound for a duration of approxi-ately 0.1 s (Fig. 4a) and the sound spannedawide frequency range

Fig. 3a). Frequencies below 600Hz are excluded by the high-passlter applied during pre-processing. Amplitude declines betweenkHz and 22kHz, the upper limit of our recording system (Fig. 3a),ut that range is important for detecting and classifying biteignals.

.1. Importance of wideband acoustic recordings

We performed a principal component analysis (PCA) on thepectra of wideband and narrowband recordings of the same bitend chew events from the two animals under study (Fig. 5). Therst two principal components accounted for 96% of the variationn the spectral signatures. In Fig. 5a, bites and chews are effectivelyeparated on wideband (020kHz) but not narrowband (08kHz)ata. In Fig. 5b, spectral characteristics are uniform across animalsn wideband data but vary significantly between animals in thearrowband data. These characteristics make wideband acousticecordings necessary for our approach to the automated detectionnd classification of bite events.

.2. Acoustic contamination of recordings.2.1. Cross-contamination from other bite soundsA total of 126 field observations were made of animal proximi-

ies while grazing. 7.1% of these observations involved two ormorenimals closer than 1m. This yields an estimated interaction ratef 3.5% for two or more animals closer than 0.5m.in Fig. 3a. Signals are displayed as time-domain waveforms and are normalized to

3.2.2. Contamination from non-target soundsA non-target noise event can intrude in two ways: (1) as a

spurious event mistaken for a target event by meeting the acous-tic detection criteria and (2) as a contaminating event overlayinga valid target event and spuriously increasing its energy level.Table 2 summarizes non-target noise sources, their capacity forspurious detection, and their magnitude of interference based onenergy level and frequency of occurrence. None of these sourceshas sufficient energy within our detection band (1722kHz) to bespuriously detected as a bite event. Intermittent sources (such asanimal vocalizations, aircraft, etc.) have a low rate of occurrenceand will not significantly contaminate the data set. Continuoussounds such as crickets, when present, will contaminate everyevent in the data set. We calculated bite energy to cricket energyratio and the resulting spurious increase inmeasured bite energy asa percentage error.We obtained bite energy to cricket energy ratiosof 105.1 and 30.2 for our two samples. The worse of these wouldincrease bite energy by (1+1/30.2)/1 =1.033, for a percentage errorof 3.3%. We consider this error level acceptable in our study.Birds No Low LowJet aircraft No Medium LowFarm

equipmentNo Medium Low

Road traffic No Medium Low

102 W.M. Clapham et al. / Computers and Electronics in Agriculture 76 (2011) 96104

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nd (2) a visual event by event comparison of automated andanual bite events for one 5min recording. Manual bite countsere validated by two independent trained observers, whoseesults correlated closely (r=0.99; P=0.0001; n=12). Fig. 6 illus-rates the comparison of manual and SIGNAL-derived total biteounts for three steers over 60min of data. Although manual andutomated bite counts differed by small amounts, the repeatedeasures analysis of variance indicated no significant difference

p=0.84) between the two techniques and no significant interac-ion (p=0.53) between count technique and individual steers. Theutomated bite counts exhibited a residual error of 9.1% relativeo the manual bite counts. In the event by event comparison, SIG-AL identified 154 true bite events (true positives; TP), detectedevents that were not bites (false positives; FP) and missed eightanually identified bite events (false negatives; FN). SIGNAL deliv-red a true positive detection rate or sensitivity (correctly detectedites/total true bites) of 0.95 (TP/(TP+ FN)) and a positive predic-ive value (correctly detected bites/total detected bites) of 0.99TP/(TP+ FP)) (Suojanen, 1999).

. Discussion

Our acoustic monitoring system recorded and processed acous-ic recordings of grazing activity in steers under free-ranging

onditions, including identifying, classifying andquantifying inges-ive events. Characteristics of the system that contribute to itsuccessful trials include: (1) a light-weight, sturdy, halter-mountedigital recorder and microphone that had no observable impact onrazing behavior, (2) CD-quality digital recordings (44.1 kHz, 16-mponent 1

ites and chews from wideband and narrowband recordings and (b) the same data

bit) that included the full frequency range up to 22kHz, and (3)SIGNAL software that could utilize the high-frequency characteris-tics of bite sounds to automatically detect and measure bite eventparameters from digital recordings of any length.

4.1. Importance of wideband acoustic recordings

Previouswork has distinguished bites and chews based on tem-poral characteristics and audible differences in sound quality (Lacaand WallisDeVries, 2000). Previous audio recordings of biting andchewing events relied on forehead-mounted, inward-facingmicro-phones (Laca and WallisDeVries, 2000; Ungar and Rutter, 2006;Galli et al., 2006; Milone et al., 2009) and were apparently limitedin frequency range. For example, Laca and WallisDeVries (2000),showbovine bite and chew spectra limited to approximately 6kHz,while Milone et al. (2009) show sheep bite, chew and chewbitespectrograms limited to approximately 8kHz.

Our approach to automatically detecting bite events and distin-guishing them from chews is founded on the fact that although thespectral profiles of bites and chews are similar below 8kHz, theydiffer significantly in the 1020kHz range.Wideband acoustic dataextending to 22kHz (Fig. 4a) show bite and chew events as spec-trally different and distinguishable, while narrowband data limitedto 8kHz show bite and chew events with similar spectral charac-teristics (Fig. 3b). Fig. 3a and b represents the same bite and chew

events and are normalized for root-mean-square (RMS) powerto emphasize differences in spectral distribution. Non-normalizedplots (data not shown) depict an even greater bitechew differ-ence in the 1020kHz range, further increasing the separabilityof bites and chews in acoustic data. For this reason, our project is

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ased on full frequency range acoustic recordings. The distinctionetween wideband and narrowband data is further confirmed byrincipal component analysis (Fig. 5). These data also suggest thatccurate automated bitechew differentiation would be difficultsing narrowband data and possibly inconsistent among animals.

.2. Acoustic contamination of recordings

In a manual analysis of individual events, spurious and con-aminated events can be identified and excluded. However, in anutomated analysis, any event meeting the mathematical selec-ion criteria will be included in the measured data set, whether aalid, spurious or contaminated event. We therefore surveyed theange and modalities of non-target noise sources with two ques-ions in mind: (1) can a non-target event be spuriously acceptednd (2) if overlayed on a target event, what would be the quanti-ative impact on event energy. Our goal was to estimate the totalmpact of non-target acoustic events on measured bite energy.

Contamination from bite sounds of adjacent animals has beenaised as an important concern (Ungar and Rutter, 2006). Our anal-sis indicates that even at a high stocking rate (40 animals per ha),

nly 3.5% of recorded bites would be contaminated at a level of% or greater (resulting from animals within 0.5m of each other).owever, our calculations do not account for three factors thatmayurther reduce contamination: (1) the reluctance of these large ani-als to bring their heads within the 0.5m critical distance of each WAV file

rdings vs. automated processing of the audio portion of the same recordings. Each

other; (2) acoustic shadowingwhen the animals heads are parallelbut opposite in orientation. In half of these instances the recordingmicrophone, mounted on the side of the jaw, will be acousticallyshadowed from the sounds of the adjacent animal by the head ofthe wearer, and (3) temporal dispersion of bite events; since bitesoccupy less than 30% of recorded duration, energy contaminationwill be reduced proportionally. Considering these factors, we esti-mate that less than 1% of our bite events will have an energy errorof 1% or greater due to contamination. (Note that contaminatingbites that do not overlay a bite event are rejected by the thresholdsetting of the bite event detector and do not enter the data stream.)Cross-contamination levels may fluctuate with stocking density,pasture geometry and herd behavior. We expect to evaluate theseassumptions further as grazing dynamics change across the season.

We analyzed contamination from non-bite noise events in twocases. First, non-bite events that do not overlay a bite event willnot meet the spectral profile of a bite and will be rejected by thebite detector. Second, when non-bite noise events do overlay biteevents, we estimated the resulting corruption of measured biteenergy. These events divide roughly into high energy events thatoccur rarely (such as aircraft flyovers and cattle vocalizations) and

low energy events that occur frequently or continuously (such ascrickets). We calculated bite energy corruption due to a continu-ous non-target source, cricket sounds and found the result was asmall percentage error. At the other extreme, the energy level ofan aircraft flyover would invalidate any simultaneous bite events,

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ut if a flyover occurs once per hour and shadows 10 out of 1000ecorded bites during the incident, the net corruption is again inhe 1% range.

.3. Automated bite detection

When we began the effort to automate the identification andlassification of the sound data, we capitalized on the fact that inn acoustic recording of grazing, bite events had significant energyetween 17 and 20kHz, a region of the sound spectrum with littleackground noise in pastoral settings. This became the foundationf our acoustic bite event detector, whichwe programmed to focusn the 1722kHz range (Fig. 3). With the detection system cali-rated for a given animal and forage, our data show no significantifference between bite counts derived from manual classifica-ion based on video/audio recordings and automated classificationsing SIGNAL. In practice, our system will require periodic manualalibration. Further tests are needed to determine the frequency ofalibration, but calibration will almost certainly be required whennimals are moved to a new forage resource, e.g., from mixed pas-ure to alfalfa. After the calibration procedure is completed, SIGNALan process long files rapidly and with high accuracy.

.4. General considerations

One limitation of the digital recording system is data storageapacity for the 44.1Hz, 16-bit recordings. 32GB SDmemory cardsan accommodate 48h of continuous data recording, but the powerupply must be increased to accommodate that duration, and aarger power supply increases the equipments footprint on theivestock. A more promising approach is to implement the detec-ion, classification and measurement algorithms on an embeddedrocessor and store this dramatically reduced data set instead ofecorded acoustic waveforms.

Development of a method to estimate grazing livestock intakes a goal that has been long sought after. Estimating forage intakes a vital step toward integrating animal performance and forageanagement in grazing systems and is important to measuresf performance efficiency. Our recording and automated pro-essing system solves major problems in estimating ingestivevents in grazing livestock, namely, recording extended periods ofree-grazing, automatically classifying bite and chew events anduantifying relative energy per bite.cknowledgements

We thank Nathan Wade Snyder for his assistance during allhases of this research, especially his critical roles with hard-nics in Agriculture 76 (2011) 96104

ware fabrication and data management; Keith Galford and DannyCarter for their contributions to animal care andmanagement; andEdward Pell and Shane Clarkson of the West Virginia UniversityWillowbend Farm for their cooperation and assistance during thisproject. We extend appreciation to Dr. Emilio Laca for providingsound samples. Mention of trade names or commercial products inthis article is solely for the purpose of providing specific informa-tion and does not imply recommendation or endorsement by theU.S. Department of Agriculture (USDA). The research was fundedin part by the USDA-Agricultural Research Service and is part ofa regional initiative, Pasture-Based Beef Systems for Appalachia, acollaboration among USDA-ARS, Virginia Tech, West Virginia Uni-versity, and Clemson University.

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Acoustic monitoring system to quantify ingestive behavior of free-grazing cattleIntroductionMethods and materialsField conditionsHardware components and setupSound file processing and analysisComparison of wideband and narrowband recordings of ingestive soundsEstimating acoustic contamination of recordingsCross-contamination from other bite soundsContamination from non-target sounds

Calibration and validation of automated bite detection

ResultsImportance of wideband acoustic recordingsAcoustic contamination of recordingsCross-contamination from other bite soundsContamination from non-target sounds

Calibration and validation of automated bite detection

DiscussionImportance of wideband acoustic recordingsAcoustic contamination of recordingsAutomated bite detectionGeneral considerations

AcknowledgementsReferences