Real-Time Detection of Overlapping Sound Events with Non-Negative Matrix Factorization

Dessein, Arnaud; Cont, Arshia; Lemaître, Guillaume

doi:10.1007/978-3-642-30232-9_14

Cited by 42 publications

(23 citation statements)

References 44 publications

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“…can be achieved through sound source separation techniques, such as non-negative matrix factorization (NMF) on time-frequency representations of the signals. NMF has been used in [7] and [8] to pre-process the signal creating a dictionary from single events, and later in [6] and [9] directly on the mixture, without learning from isolated sounds. The work in [9] was extended in [10] making learning feasible for long recordings by reducing the dictionary size.…”

Section: Introductionmentioning

confidence: 99%

Recurrent neural networks for polyphonic sound event detection in real life recordings

Parascandolo

Huttunen

Virtanen

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

298

184

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In this paper we present an approach to polyphonic sound event detection in real life recordings based on bi-directional long short term memory (BLSTM) recurrent neural networks (RNNs). A single multilabel BLSTM RNN is trained to map acoustic features of a mixture signal consisting of sounds from multiple classes, to binary activity indicators of each event class. Our method is tested on a large database of real-life recordings, with 61 classes (e.g. music, car, speech) from 10 different everyday contexts. The proposed method outperforms previous approaches by a large margin, and the results are further improved using data augmentation techniques. Overall, our system reports an average F 1-score of 65.5% on 1 second blocks and 64.7% on single frames, a relative improvement over previous state-of-the-art approach of 6.8% and 15.1% respectively.

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Section: Introductionmentioning

confidence: 99%

Recurrent neural networks for polyphonic sound event detection in real life recordings

Parascandolo

Huttunen

Virtanen

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

298

184

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“…For audio event detection, generative models such as NMF [14], probabilistic latent component analysis (PLCA) [15] and hidden Markov model (HMM) [16] are used with inspiration from music transcription. Neural network methods including recurrent neural networks (RNNs) [17] and bidictionary long-short term memory (BLSTM) [18] are also used for event detection.…”

Section: Related Workmentioning

confidence: 99%

A joint detection-classification model for audio tagging of weakly labelled data

Kong

Wang

et al. 2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Audio tagging aims to assign one or several tags to an audio clip. Most of the datasets are weakly labelled, which means only the tags of the clip are known, without knowing the occurrence time of the tags. The labeling of an audio clip is often based on the audio events in the clip and no event level label is provided to the user. Previous works have used the bag of frames model assume the tags occur all the time, which is not the case in practice. We propose a joint detection-classification (JDC) model to detect and classify the audio clip simultaneously. The JDC model has the ability to attend to informative and ignore uninformative sounds. Then only informative regions are used for classification. Experimental results on the “CHiME Home” dataset show that the JDC model reduces the equal error rate (EER) from 19.0% to 16.9%. More interestingly, the audio event detector is trained successfully without needing the event level label

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“…In [8,7] this problem has been addressed by means of adjust ing the f3 parameter of the Bregman divergence. But this solution does not allow an intuitive control.…”

Section: Controlling the Noise Robustness Of Is-nmdmentioning

confidence: 99%

“…In most studies the Kullback-Leibler (KL) divergence is favoured [5,7]. Alternatively the generalised beta divergence has been used [8] and especially the Itakura Saito divergence (IS) [9,10].…”

Section: Introductionmentioning

confidence: 99%

On automatic drum transcription using non-negative matrix deconvolution and itakura saito divergence

Röebel

Pons

Liuni

et al. 2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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International audienceThis paper presents an investigation into the detection and classification of drum sounds in polyphonic music and drum loops using non-negative matrix deconvolution (NMD) and the Itakura Saito divergence. The Itakura Saito divergence has recently been proposed as especially appropriate for decomposing audio spectra due to the fact that it is scale invariant, but it has not yet been widely adopted. The article studies new contributions for audio event detection methods using the Itakura Saito divergence that improve efficiency and numerical stability, and simplify the generation of target pattern sets. A new approach for handling background sounds is proposed and moreover, a new detection criteria based on estimating the perceptual presence of the target class sources is introduced. Experimental results obtained for drum detection in polyphonic music and drum soli demonstrate the beneficial effects of the proposed extensions

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Real-Time Detection of Overlapping Sound Events with Non-Negative Matrix Factorization

Cited by 42 publications

References 44 publications

Recurrent neural networks for polyphonic sound event detection in real life recordings

Recurrent neural networks for polyphonic sound event detection in real life recordings

A joint detection-classification model for audio tagging of weakly labelled data

On automatic drum transcription using non-negative matrix deconvolution and itakura saito divergence

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