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Plant classification from bat-like echolocation signals.

Yovel Y, Franz MO, Stilz P, Schnitzler HU - PLoS Comput. Biol. (2008)

Bottom Line: We used a standard machine learning algorithm (SVM) to automatically extract suitable linear combinations of time and frequency cues from the spectrograms such that classification with high accuracy is enabled.This demonstrates that ultrasonic echoes are highly informative about the species membership of an ensonified plant, and that this information can be extracted with rather simple, biologically plausible analysis.Thus, our findings provide a new explanatory basis for the poorly understood observed abilities of bats in classifying vegetation and other complex objects.

View Article: PubMed Central - PubMed

Affiliation: Animal Physiology, Zoological Institute, University of Tuebingen, Tuebingen, Germany.

ABSTRACT
Classification of plants according to their echoes is an elementary component of bat behavior that plays an important role in spatial orientation and food acquisition. Vegetation echoes are, however, highly complex stochastic signals: from an acoustical point of view, a plant can be thought of as a three-dimensional array of leaves reflecting the emitted bat call. The received echo is therefore a superposition of many reflections. In this work we suggest that the classification of these echoes might not be such a troublesome routine for bats as formerly thought. We present a rather simple approach to classifying signals from a large database of plant echoes that were created by ensonifying plants with a frequency-modulated bat-like ultrasonic pulse. Our algorithm uses the spectrogram of a single echo from which it only uses features that are undoubtedly accessible to bats. We used a standard machine learning algorithm (SVM) to automatically extract suitable linear combinations of time and frequency cues from the spectrograms such that classification with high accuracy is enabled. This demonstrates that ultrasonic echoes are highly informative about the species membership of an ensonified plant, and that this information can be extracted with rather simple, biologically plausible analysis. Thus, our findings provide a new explanatory basis for the poorly understood observed abilities of bats in classifying vegetation and other complex objects.

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Related in: MedlinePlus

Raw data after preprocessing.In all rows the species from left to right are: apple, spruce, blackthorn, beech, and corn field. In all spectrograms, color bars are in dB. The units in the time signals are arbitrary. (A) The average spectrogram of each plant species. (B) The average envelope of the time signal of each plant species. (C) The corresponding example of a single spectrogram of each plant species (the effect of applying the threshold is noticeable). (D) The corresponding example of a single echo of each tree in the time domain.
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pcbi-1000032-g010: Raw data after preprocessing.In all rows the species from left to right are: apple, spruce, blackthorn, beech, and corn field. In all spectrograms, color bars are in dB. The units in the time signals are arbitrary. (A) The average spectrogram of each plant species. (B) The average envelope of the time signal of each plant species. (C) The corresponding example of a single spectrogram of each plant species (the effect of applying the threshold is noticeable). (D) The corresponding example of a single echo of each tree in the time domain.

Mentions: The next step was intended to reduce the noise, and to avoid possible classification artifacts. This issue is not trivial, since the recordings of different plant species differed in their noise characteristics. There are many reasons for these species-specific noise characteristic. The recording of different species on different days can result in temperature variations of the environment which in turn leads to a different atmospheric attenuation. The varying recording locations can create a species-specific background noise. The noise characteristics also depend on the recording parameters, since two of the plants were recorded with a gain that was 2.5 times lower than the other three. Indeed a control experiment showed that a classification above chance level was possible by using spectrogram regions that contained only noise. The first noise reducing step was actually obtained in the first preprocess described above of cutting out the echo in the time domain. By doing this we ensured that only the parts of the echo that had a certain level above the noise went through any following analysis. We now aimed to exclude noise regions from the spectrograms frequency-time domain. To do so we computed the magnitude of the spectrogram of the noise signal of each echo (using the last 5000 time samples of the signal). We then selected for every spectrogram the maximum noise intensity at each frequency, thus calculating the maximum noise spectrum. This maximum noise spectrum was used as a threshold. For each time bin (i.e. column of the spectrogram) we set to zero any pixel of the spectrogram that was lower than five times the value of the maximum noise spectrum at that particular frequency. This procedure actually zeroed major parts of the spectrogram, thus ensuring that our classifier was only using the parts of the echo that were significantly above the noise level. For further comments regarding classification according to noise see the discussion section. Figure 10 shows examples of acquired data after the preprocessing.


Plant classification from bat-like echolocation signals.

Yovel Y, Franz MO, Stilz P, Schnitzler HU - PLoS Comput. Biol. (2008)

Raw data after preprocessing.In all rows the species from left to right are: apple, spruce, blackthorn, beech, and corn field. In all spectrograms, color bars are in dB. The units in the time signals are arbitrary. (A) The average spectrogram of each plant species. (B) The average envelope of the time signal of each plant species. (C) The corresponding example of a single spectrogram of each plant species (the effect of applying the threshold is noticeable). (D) The corresponding example of a single echo of each tree in the time domain.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2267002&req=5

pcbi-1000032-g010: Raw data after preprocessing.In all rows the species from left to right are: apple, spruce, blackthorn, beech, and corn field. In all spectrograms, color bars are in dB. The units in the time signals are arbitrary. (A) The average spectrogram of each plant species. (B) The average envelope of the time signal of each plant species. (C) The corresponding example of a single spectrogram of each plant species (the effect of applying the threshold is noticeable). (D) The corresponding example of a single echo of each tree in the time domain.
Mentions: The next step was intended to reduce the noise, and to avoid possible classification artifacts. This issue is not trivial, since the recordings of different plant species differed in their noise characteristics. There are many reasons for these species-specific noise characteristic. The recording of different species on different days can result in temperature variations of the environment which in turn leads to a different atmospheric attenuation. The varying recording locations can create a species-specific background noise. The noise characteristics also depend on the recording parameters, since two of the plants were recorded with a gain that was 2.5 times lower than the other three. Indeed a control experiment showed that a classification above chance level was possible by using spectrogram regions that contained only noise. The first noise reducing step was actually obtained in the first preprocess described above of cutting out the echo in the time domain. By doing this we ensured that only the parts of the echo that had a certain level above the noise went through any following analysis. We now aimed to exclude noise regions from the spectrograms frequency-time domain. To do so we computed the magnitude of the spectrogram of the noise signal of each echo (using the last 5000 time samples of the signal). We then selected for every spectrogram the maximum noise intensity at each frequency, thus calculating the maximum noise spectrum. This maximum noise spectrum was used as a threshold. For each time bin (i.e. column of the spectrogram) we set to zero any pixel of the spectrogram that was lower than five times the value of the maximum noise spectrum at that particular frequency. This procedure actually zeroed major parts of the spectrogram, thus ensuring that our classifier was only using the parts of the echo that were significantly above the noise level. For further comments regarding classification according to noise see the discussion section. Figure 10 shows examples of acquired data after the preprocessing.

Bottom Line: We used a standard machine learning algorithm (SVM) to automatically extract suitable linear combinations of time and frequency cues from the spectrograms such that classification with high accuracy is enabled.This demonstrates that ultrasonic echoes are highly informative about the species membership of an ensonified plant, and that this information can be extracted with rather simple, biologically plausible analysis.Thus, our findings provide a new explanatory basis for the poorly understood observed abilities of bats in classifying vegetation and other complex objects.

View Article: PubMed Central - PubMed

Affiliation: Animal Physiology, Zoological Institute, University of Tuebingen, Tuebingen, Germany.

ABSTRACT
Classification of plants according to their echoes is an elementary component of bat behavior that plays an important role in spatial orientation and food acquisition. Vegetation echoes are, however, highly complex stochastic signals: from an acoustical point of view, a plant can be thought of as a three-dimensional array of leaves reflecting the emitted bat call. The received echo is therefore a superposition of many reflections. In this work we suggest that the classification of these echoes might not be such a troublesome routine for bats as formerly thought. We present a rather simple approach to classifying signals from a large database of plant echoes that were created by ensonifying plants with a frequency-modulated bat-like ultrasonic pulse. Our algorithm uses the spectrogram of a single echo from which it only uses features that are undoubtedly accessible to bats. We used a standard machine learning algorithm (SVM) to automatically extract suitable linear combinations of time and frequency cues from the spectrograms such that classification with high accuracy is enabled. This demonstrates that ultrasonic echoes are highly informative about the species membership of an ensonified plant, and that this information can be extracted with rather simple, biologically plausible analysis. Thus, our findings provide a new explanatory basis for the poorly understood observed abilities of bats in classifying vegetation and other complex objects.

Show MeSH
Related in: MedlinePlus