Foreground-Background Ambient Sound Scene Separation

Olvera, Michel; Serizel, Romain; Gasso, Gilles

doi:10.23919/eusipco47968.2020.9287436

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…There are two types of sound separation systems. The first type of system uses as many outputs as the number of possible SE classes [25], [26], [28], [32]. Such systems perform both separation and identification and could thus be used directly for TSE.…”

Section: A Source Separationmentioning

confidence: 99%

SoundBeam: Target sound extraction conditioned on sound-class labels and enrollment clues for increased performance and continuous learning

Delcroix¹,

Vázquez²,

Ochiai³

et al. 2022

Preprint

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In many situations, we would like to hear desired sound events (SEs) while being able to ignore interference. Target sound extraction (TSE) aims at tackling this problem by estimating the sound of target SE classes in a mixture while suppressing all other sounds. We can achieve this with a neural network that extracts the target SEs by conditioning it on clues representing the target SE classes. Two types of clues have been proposed, i.e., target SE class labels and enrollment sound samples similar to the target sound. Systems based on SE class labels can directly optimize embedding vectors representing the SE classes, resulting in high extraction performance. However, extending these systems to the extraction of new SE classes not encountered during training is not easy. Enrollment-based approaches extract SEs by finding sounds in the mixtures that share similar characteristics to the enrollment. These approaches do not explicitly rely on SE class definitions and can thus handle new SE classes. In this paper, we introduce a TSE framework, SoundBeam, that combines the advantages of both approaches. We also perform an extensive evaluation of the different TSE schemes using synthesized and real mixtures, which shows the potential of SoundBeam.

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Section: A Source Separationmentioning

confidence: 99%

SoundBeam: Target sound extraction conditioned on sound-class labels and enrollment clues for increased performance and continuous learning

Delcroix¹,

Vázquez²,

Ochiai³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…[38,39]). Consequently, a variety of solutions have also been explored, including the use of transfer learning following a pretraining using general-purpose datasets [37,40,41], source separation and denoising [42,43], discriminative training [44], domain adaptation based on covariance normalization [45], unsupervised domain adaptation [46,47], domain/context adaptive neural networks [36,48], etc. Use of data augmentation techniques to address domain mismatch problems was common among the top-performing solutions in a public challenge focused on developing generalizable methods for detecting birds [49].…”

Section: Introductionmentioning

confidence: 99%

Extensive data engineering to the rescue: building a multi-species katydid detector from unbalanced, atypical training datasets

Madhusudhana,

Klinck,

Symes

2024

Phil. Trans. R. Soc. B

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Passive acoustic monitoring (PAM) is a powerful tool for studying ecosystems. However, its effective application in tropical environments, particularly for insects, poses distinct challenges. Neotropical katydids produce complex species-specific calls, spanning mere milliseconds to seconds and spread across broad audible and ultrasonic frequencies. However, subtle differences in inter-pulse intervals or central frequencies are often the only discriminatory traits. These extremities, coupled with low source levels and susceptibility to masking by ambient noise, challenge species identification in PAM recordings. This study aimed to develop a deep learning-based solution to automate the recognition of 31 katydid species of interest in a biodiverse Panamanian forest with over 80 katydid species. Besides the innate challenges, our efforts were also encumbered by a limited and imbalanced initial training dataset comprising domain-mismatched recordings. To overcome these, we applied rigorous data engineering, improving input variance through controlled playback re-recordings and by employing physics-based data augmentation techniques, and tuning signal-processing, model and training parameters to produce a custom well-fit solution. Methods developed here are incorporated into Koogu, an open-source Python-based toolbox for developing deep learning-based bioacoustic analysis solutions. The parametric implementations offer a valuable resource, enhancing the capabilities of PAM for studying insects in tropical ecosystems. This article is part of the theme issue ‘Towards a toolkit for global insect biodiversity monitoring’.

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“…This enables the representation of non-stationary audio waveforms, by their nature, as stationary frames of the signal. In commonly used methods for audio signal processing such as speech, music genre, animal, or ambient sound recognition, the typical frame length is from 10 ms to 40 ms [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Cepstral coefficients effectiveness for gunshot classifying

Svatos,

Holub

2024

Meas. Sci. Technol.

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This paper analyses the efficiency of various frequency cepstral coefficients (fCC) in a non-speech application, specifically in classifying acoustic impulse events - gunshots. There are various methods for such event identification available. The majority of these methods are based on time or frequency domain algorithms. However, both of these domains have their limitations and disadvantages. In this article, an fCC, combining the advantages of both frequency and time domains, is presented and analyzed. These originally speech features showed potential not only in speech-related applications but also in other acoustic applications. The comparison of the classification efficiency based on features obtained using four different fCC, namely Mel-frequency Cepstral Coefficients (MFCC), Inverse Mel-frequency Cepstral Coefficients (IMFCC), Linear-frequency Cepstral Coefficients (LFCC), and Gammatone-frequency Cepstral Coefficients (GTCC) is presented. An optimal frame length for an fCC calculation is also explored. Various gunshots from short guns and rifle guns of different calibers and multiple acoustic impulse events, similar to the gunshots, to represent false alarms are used. More than six hundred acoustic events records have been acquired and used for training and validation of two designed classifiers, Support Vector Machine, and Neural Network. Accuracy, Recall and Matthew's correlation coefficient measure the classification success rate. The results reveal the superiority of GFCC to other analyzed methods.

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Foreground-Background Ambient Sound Scene Separation

Cited by 7 publications

References 19 publications

SoundBeam: Target sound extraction conditioned on sound-class labels and enrollment clues for increased performance and continuous learning

SoundBeam: Target sound extraction conditioned on sound-class labels and enrollment clues for increased performance and continuous learning

Extensive data engineering to the rescue: building a multi-species katydid detector from unbalanced, atypical training datasets

Cepstral coefficients effectiveness for gunshot classifying

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