Unsupervised Filterbank Learning Using Convolutional Restricted Boltzmann Machine for Environmental Sound Classification

Sailor, Hardik B.; Agrawal, Dharmesh M.; Patil, Hemant A.

doi:10.21437/interspeech.2017-831

Cited by 72 publications

(31 citation statements)

References 27 publications

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“…96.7 ------ [39] ------88.50 [46] ------84.90 [35] ------86.50 [36] ------83.50 [38] ------83.50 [37] ---…”

Section: Resultsmentioning

confidence: 99%

“…ESC-50 also contains unlabeled samples for unsupervised learning. Sailor et al [35] have applied deep CNNs to this classification task and have achieved results superior to human classification. Li et al [36] proposed a neural network, which took raw audio signals, spectrograms, and audio features as input, and combined it with an attention mechanism that was trained end-toend with the rest of the network.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble of convolutional neural networks to improve animal audio classification

Nanni

Costa

Aguiar

et al. 2020

J AUDIO SPEECH MUSIC PROC.

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Recently, deep learning classifiers have proven even more robust in pattern recognition and classification than have texture analysis techniques. With the broad availability of relatively inexpensive Graphics Processing Units (GPUs), many researchers have begun applying deep learning techniques to visual representations of acoustic traces. Preselected or handcrafted descriptors, such as LBP, are not necessary for deep learners since they learn salient features during the training phase. Deep learners, moreover, are uniquely suited to handling visual representations of audio because many of the most famous deep classifiers, such as Convolutional Neural Networks (CNN), require matrices as their input. Humphrey and Bello [17, 18] were among the first to apply CNNs to audio images for music classification and, as a result, succeeded in redefining the state of the art in automatic chord detection and recognition. In the same year, Nakashika et al. [19] reported converting spectrograms to GCLM maps to train CNNs to performed music genre classification on the GTZAN dataset [20]. Later, Costa et al. [21] fused a CNN with the traditional pattern recognition framework of training SVMs on LBP features to classify the LMD dataset. These works exceeded traditional classification results on these genre datasets. Up to this point, most work in audio classification has applied the latest advances in machine learning to the problem of sound classification and recognition without modifying the classification process to make it singularly suitable for sound recognition. An early exception to the generic approach is found in the work of Sigtia and Dixon [22], who adjusted CNN parameters and structures in such a way as to reduce the time it took to train a set of audio images. Time reduction was accomplished by replacing

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“…96.7 ------ [39] ------88.50 [46] ------84.90 [35] ------86.50 [36] ------83.50 [38] ------83.50 [37] ---…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensemble of convolutional neural networks to improve animal audio classification

Nanni

Costa

Aguiar

et al. 2020

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

show abstract

“…In addition, TS-CNN10-concat and ST-CNN10-concat apply the attention mechanisms of temporalspectral concatenation in Figure 2(a) and spectral-temporal concatenation in Figure 2(b), respectively. [19] 80.5% 64.9% 73.0% EnvNet-v2 [29] 91.3% 84.9% 78.3% SB-CNN [30] 91.7% 83.9% 83.7% GTSC+TEO-GTSC [31] -81.9% 88.0% ConvRBM+FBEs [32] -86.5% -ACRNN [12] 93.7% 86.1% -Multi-Stream CNN [33] 94.2% 84.0% -MelFB+LGTFB-EN-CNN [34] 93.7% 88.1% 85.8% Human [3] 95.7% 81.3% -CNN10 [23] 92 [21] 59.7% 62.5% CNN10 [23] 68.1%…”

Section: Network Structuresmentioning

confidence: 99%

Environmental Sound Classification with Parallel Temporal-Spectral Attention

Wang¹,

Zou²,

Chong³

et al. 2020

Interspeech 2020

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Convolutional neural networks (CNN) are one of the bestperforming neural network architectures for environmental sound classification (ESC). Recently, temporal attention mechanisms have been used in CNN to capture the useful information from the relevant time frames for audio classification, especially for weakly labelled data where the onset and offset times of the sound events are not applied. In these methods, however, the inherent spectral characteristics and variations are not explicitly exploited when obtaining the deep features. In this paper, we propose a novel parallel temporal-spectral attention mechanism for CNN to learn discriminative sound representations, which enhances the temporal and spectral features by capturing the importance of different time frames and frequency bands. Parallel branches are constructed to allow temporal attention and spectral attention to be applied respectively in order to mitigate interference from the segments without the presence of sound events. The experiments on three environmental sound classification (ESC) datasets and two acoustic scene classification (ASC) datasets show that our method improves the classification performance and also exhibits robustness to noise.

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“…The moment parameters of Adam optimization was chosen to be β1=0.5, and β2=0.999. The annealing dropout probability was chosen to be 0.3 based on our earlier experiments in the ASR [20] and environmental sound classification [30]. After the model was trained, the features were extracted from the speech signal as discussed in Section 2.2.…”

Section: Training Of Convrbm and Feature Extractionmentioning

confidence: 99%

Auditory Filterbank Learning for Temporal Modulation Features in Replay Spoof Speech Detection

2018

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In this paper, we present a standalone replay spoof speech detection (SSD) system to classify the natural vs. replay speech. The replay speech spectrum is known to be affected in the higher frequency range. In this context, we propose to exploit an auditory filterbank learning using Convolutional Restricted Boltzmann Machine (ConvRBM) with the pre-emphasized speech signals. Temporal modulations in amplitude (AM) and frequency (FM) are extracted from the ConvRBM subbands using the Energy Separation Algorithm (ESA). ConvRBM-based short-time AM and FM features are developed using cepstral processing, denoted as AM-ConvRBM-CC and FM-ConvRBM-CC. Proposed temporal modulation features performed better than the baseline Constant-Q Cepstral Coefficients (CQCC) features. On the evaluation set, an absolute reduction of 7.48 % and 5.28 % in Equal Error Rate (EER) is obtained using AM-ConvRBM-CC and FM-ConvRBM-CC, respectively compared to our CQCC baseline. The best results are achieved by combining scores from AM and FM cues (0.82 % and 8.89 % EER for development and evaluation set, respectively). The statistics of AM-FM features are analyzed to understand the performance gap and complementary information in both the features.

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Unsupervised Filterbank Learning Using Convolutional Restricted Boltzmann Machine for Environmental Sound Classification

Cited by 72 publications

References 27 publications

Ensemble of convolutional neural networks to improve animal audio classification

Ensemble of convolutional neural networks to improve animal audio classification

Environmental Sound Classification with Parallel Temporal-Spectral Attention

Auditory Filterbank Learning for Temporal Modulation Features in Replay Spoof Speech Detection

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