Classification of whale vocalizations using the Weyl transform

Xian, Yin; Thompson, Andrew; Qiu, Qiang; Nolte, L. W.; Nowacek, Douglas P.; Lu, Jianfeng; Calderbank, Robert

doi:10.1109/icassp.2015.7178074

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…The result obtained presents enhanced sensitivity and false discovery rate performance besides showing a low computational complexity in comparison to the LPC-HMM and the MFCC-HMM detectors. Others are Teager energy operator (TEO) [44], [94], Weyl transform (WyT) [95].…”

Section: G Other Feature Extraction Methodsmentioning

confidence: 99%

Review of Automatic Detection and Classification Techniques for Cetacean Vocalization

2020

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Cetaceans have elicited the attention of researchers in recent decades due to their importance to the ecosystem and their economic values. They use sound for communication, echolocation and other social activities. Their sounds are highly non-stationary, transitory and range from short to long sounds. Passive acoustic monitoring (PAM) is a popular method used for monitoring cetaceans in their ecosystems. The volumes of data accumulated using PAM are usually big, so they are difficult to analyze using manual inspection. Therefore different techniques with mixed outcomes have been developed for the automatic detection and classification of signals of different cetacean species. So far, no single technique developed is perfect to detect and classify the vocalizations of over 82 known species due to variability in time-frequency, difference in the amplitude among species and within species' vocal repertoire, physical environment, among others. The accuracy of any detector or classifier depends on the technique adopted as well as the nature of the signal to be analyzed. In this article, we review the existing techniques for the automatic detection and classification of cetacean vocalizations. We categorize the surveyed techniques, while emphasizing the advantages and disadvantages of these techniques. The article suggests possible research directions that can improve existing detection and classification techniques. In addition, the article recommends other suitable techniques that can be used to analyze non-linear and non-stationary signals such as the cetaceans' signals. Several research have been dedicated to this topic, however, there is no review of these past results that gives a quick overview in the area of cetacean detection and classification. This review will help researchers and practitioners in the field to make insightful decisions based on their requirements.

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Section: G Other Feature Extraction Methodsmentioning

confidence: 99%

Review of Automatic Detection and Classification Techniques for Cetacean Vocalization

2020

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“…However, human annotation is still necessary for the creation of training databases. Most methods for detecting and classifying clicks, whistles and pulsed calls from marine mammals have used standard audio analysis methods, such as the short-term Fourier transform [24], the wavelet transform [25], the Hilbert Huang transform [26], the Chirplet transform [27] and the Weyl transform [28]. The Hidden Markov Models (HMM) [29], the Support Vector Machine (SVM) [30] and the Deep Neural Network (DNN) models [31] were used to classify the audio features.…”

Section: Introductionmentioning

confidence: 99%

Spatial, Temporal and Spectral Multiresolution Analysis for the INTERSPEECH 2019 ComParE Challenge

Caraty¹,

Montacié²

2019

Interspeech 2019

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The INTERSPEECH 2019 Orca Activity Challenge consists in the detection of the Orca sounds from underwater audio signal. Orca can produce a wide variety of sounds categorized in clicks, whistles and pulsed calls. Clicks are useful for echolocation, whistles and pulsed calls are used as social signals. Experiments were conducted on DeepAL Fieldwork Data (DLFD). Underwater sounds were recorded in northern British Columbia by a hydrophones array. Recordings were labeled by marine biologists in Orca sounds or Noise. We have investigated multiresolution analysis according to the three main relevant acoustic levels: spatial, temporal and spectral. For this purpose, we studied the beamforming array analysis, the multitemporal resolution and the multilevel wavelet decomposition. For the spatial level, a beamforming algorithm was used for denoising the underwater audio signal. For the temporal level, two sets of multitemporal three-level features were extracted using pyramidal representation. For the spectral level, in order to detect transient sound, wavelet analysis was computed using various wavelet families. At last, an Orca Activity detector was designed combining ComParE set with multitemporal and multilevel wavelet features. Experiments on the Test set have shown a significant improvement of 0.051, compared to the baseline performance of the Challenge (0.866).

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“…Huang et al [2014b] proposed a method to classification whales using a combination of k-means clustering and several features: center frequency, upper and lower frequency limits, as well as amplitude-weighted mean frequency. Xian et al [2015] applied the Weyl transform to represent the vocalization of marine mammals, which can capture the global information of signals. Ou et al [2015] used the spectrograms generated with the Pseudo Wigner-Ville Distribution (PWVD) to represent the Ballen whale downsweep calls, which provided much higher simultaneous time-frequency resolution.…”

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confidence: 99%

Acoustic classification of Australian frogs for ecosystem survey

Xie¹

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Frogs play an important role in Earth's ecosystem, but the decline of their population has been spotted at many locations around the world. Monitoring frog activity can assist conservation efforts, and improve our understanding of their interactions with the environment and other organisms. Traditional observation methods require ecologists and volunteers to visit the field, which greatly limit the scale for acoustic data collection. Recent advances in acoustic sensors provide a novel method to survey vocalising animals such as frogs. Once sensors are successfully installed in the field, acoustic data can be automatically collected at large spatial and temporal scales. For each acoustic sensor, several gigabytes of compressed audio data can be generated per day, and thus large volumes of raw acoustic data are collected.To gain insights about frogs and the environment, classifying frog species in acoustic data is necessary. However, manual species identification is unfeasible due to the large amount of collected data, and enabling automated species classification has become very important.Previous studies on signal processing and machine learning for frog call classification often have two limitations: (1) the recordings used to train and test classifiers are trophy recordings ( signal-to-noise ratio (SNR) (≥ 15 dB); (2) each individual recording is assumed to contain only one frog species. However, field recordings typically have a low SNR (< 15 dB) and contain multiple simultaneously vocalising frog species. This thesis aims to address two limitations and makes the following contributions.(1) Develop a combined feature set from temporal, perceptual, and cepstral domains for improving the state-of-the-art performance of frog call classification using trophy recordings (Chapter 3).(2) Propose a novel cepstral feature via adaptive frequency scaled wavelet packet decomposition (WPD) to improve cepstral feature's anti-noise ability for frog call classification using both trophy and field recordings (Chapter 4). v (3) Design a novel multiple-instance multiple-label (MIML) framework to classify multiple simultaneously vocalising frog species in field recordings (Chapter 5).(4) Design a novel multiple-label (ML) framework to increase the robustness of classification results when classifying multiple simultaneously vocalising frog species in field recordings (Chapter 6).Our proposed approaches achieve promising classification results compared with previous studies. With our developed classification techniques, the ecosystem at large spatial and temporal scales can be surveyed, which can help ecologists better understand the ecosystem.vi Keywords

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Classification of whale vocalizations using the Weyl transform

Cited by 6 publications

References 18 publications

Review of Automatic Detection and Classification Techniques for Cetacean Vocalization

Review of Automatic Detection and Classification Techniques for Cetacean Vocalization

Spatial, Temporal and Spectral Multiresolution Analysis for the INTERSPEECH 2019 ComParE Challenge

Acoustic classification of Australian frogs for ecosystem survey

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