Separation of speech from interfering sounds based on oscillatory correlation

Wang, DeLiang L.; Brown, Guy J.

doi:10.1109/72.761727

Cited by 232 publications

(12 citation statements)

References 42 publications

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“…First, the proposed system is evaluated in the pitch range estimation process with utterances chosen from the Lee's database [23] and a corpus of 100 mixtures of speech and interference [24], commonly used for CASA research, see, e.g., [13,25,26]. The corpus contains utterances from both male and female speakers.…”

Section: Pitch Range Estimationmentioning

confidence: 99%

Single channel speech separation in modulation frequency domain based on a novel pitch range estimation method

Mahmoodzadeh

Abutalebi

Soltanian-Zadeh

et al. 2012

EURASIP J. Adv. Signal Process.

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Computational Auditory Scene Analysis (CASA) has been the focus in recent literature for speech separation from monaural mixtures. The performance of current CASA systems on voiced speech separation strictly depends on the robustness of the algorithm used for pitch frequency estimation. We propose a new system that estimates pitch (frequency) range of a target utterance and separates voiced portions of target speech. The algorithm, first, estimates the pitch range of target speech in each frame of data in the modulation frequency domain, and then, uses the estimated pitch range for segregating the target speech. The method of pitch range estimation is based on an onset and offset algorithm. Speech separation is performed by filtering the mixture signal with a mask extracted from the modulation spectrogram. A systematic evaluation shows that the proposed system extracts the majority of target speech signal with minimal interference and outperforms previous systems in both pitch extraction and voiced speech separation.

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Section: Pitch Range Estimationmentioning

confidence: 99%

Single channel speech separation in modulation frequency domain based on a novel pitch range estimation method

Mahmoodzadeh

Abutalebi

Soltanian-Zadeh

et al. 2012

EURASIP J. Adv. Signal Process.

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“…The problem of discriminating music from audio has increasingly become very important as automatic audio signal recognition (ASR) systems and it has been increasingly applied in the domain of real-world multimedia [7]. Human's ear can easily distinguish audio without any influence of the mixed music [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Due to the new methods of the analysis and the synthesis processing of audio signals, the processing of musical signals has gained particular weight [16,24], and therefore, the classical sound analysis methods may be used in the processing of musical signals [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Classification and Separation of Audio and Music Signals

Al-Shoshan¹

2021

Multimedia Information Retrieval

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This chapter addresses the topic of classification and separation of audio and music signals. It is a very important and a challenging research area. The importance of classification process of a stream of sounds come up for the sake of building two different libraries: speech library and music library. However, the separation process is needed sometimes in a cocktail-party problem to separate speech from music and remove the undesired one. In this chapter, some existed algorithms for the classification process and the separation process are presented and discussed thoroughly. The classification algorithms will be divided into three categories. The first category includes most of the real time approaches. The second category includes most of the frequency domain approaches. However, the third category introduces some of the approaches in the time-frequency distribution. The approaches of time domain discussed in this chapter are the short-time energy (STE), the zero-crossing rate (ZCR), modified version of the ZCR and the STE with positive derivative, the neural networks, and the roll-off variance. The approaches of the frequency spectrum are specifically the roll-off of the spectrum, the spectral centroid and the variance of the spectral centroid, the spectral flux and the variance of the spectral flux, the cepstral residual, and the delta pitch. The time-frequency domain approaches have not been yet tested thoroughly in the process of classification and separation of audio and music signals. Therefore, the spectrogram and the evolutionary spectrum will be introduced and discussed. In addition, some algorithms for separation and segregation of music and audio signals, like the independent Component Analysis, the pitch cancelation and the artificial neural networks will be introduced.

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“…These systems extract relevant cues from a scene, such as its spectral content, spatial structure as well as temporal dynamics; hence allowing sound events with uncorrelated acoustic behavior to occupy different subspaces in the analysis stage. These models are quite effective in replicating perceptual results of stream segregation especially using simple tone and noise stimuli [32–37]. Some models also extend beyond early acoustic features to examine feature binding mechanisms that can be used as an effective strategy in segregating wide range of stimuli from simple tone sequences to spectro-temporally complex sounds like speech and music [38–40].…”

Section: Introductionmentioning

confidence: 99%

A Gestalt inference model for auditory scene segregation

Chakrabarty

Elhilali

2019

PLoS Comput Biol

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Our current understanding of how the brain segregates auditory scenes into meaningful objects is in line with a Gestaltism framework. These Gestalt principles suggest a theory of how different attributes of the soundscape are extracted then bound together into separate groups that reflect different objects or streams present in the scene. These cues are thought to reflect the underlying statistical structure of natural sounds in a similar way that statistics of natural images are closely linked to the principles that guide figure-ground segregation and object segmentation in vision. In the present study, we leverage inference in stochastic neural networks to learn emergent grouping cues directly from natural soundscapes including speech, music and sounds in nature. The model learns a hierarchy of local and global spectro-temporal attributes reminiscent of simultaneous and sequential Gestalt cues that underlie the organization of auditory scenes. These mappings operate at multiple time scales to analyze an incoming complex scene and are then fused using a Hebbian network that binds together coherent features into perceptually-segregated auditory objects. The proposed architecture successfully emulates a wide range of well established auditory scene segregation phenomena and quantifies the complimentary role of segregation and binding cues in driving auditory scene segregation.

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Separation of speech from interfering sounds based on oscillatory correlation

Cited by 232 publications

References 42 publications

Single channel speech separation in modulation frequency domain based on a novel pitch range estimation method

Single channel speech separation in modulation frequency domain based on a novel pitch range estimation method

Classification and Separation of Audio and Music Signals

A Gestalt inference model for auditory scene segregation

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