A Speech-Music Discriminator Using HILN Model Based Features

Thoshkahna, Balaji; Sudha, V; Ramakrishnan, K. R.

doi:10.1109/icassp.2006.1661303

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…1) Approaches in Literature: The features used for speechmusic classification can be either handcrafted features [20], [21], [22], [23], [24] or spectral features [25], [26], [27]. In some cases, as in real-time processing [20], efficiency is key, while in other cases, complexity can be afforded for the sake of accuracy.…”

Section: A Speech-music Classificationmentioning

confidence: 99%

Chirp Group Delay-Based Onset Detection in Instruments with Fast Attack

Joysingh¹,

Vijayalakshmi²,

Nagarajan

2022

Circuits Syst Signal Process

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A novel feature, based on the chirp z-transform, that offers an improved representation of the underlying true spectrum is proposed. This feature, the chirp MFCC, is derived by computing the Mel frequency cepstral coefficients from the chirp magnitude spectrum, instead of the Fourier transform magnitude spectrum. The theoretical foundations for the proposal, and the experimental validation using product of likelihood Gaussians, to show the improved class separation offered by the proposed chirp MFCC, when compared with vanilla MFCC are discussed. Further, real world evaluation of the feature is performed using three diverse tasks, namely, speech-music classification, speaker identification, and speech commands recognition. It is shown in all three tasks that the proposed chirp MFCC offers considerable improvements.

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Section: A Speech-music Classificationmentioning

confidence: 99%

Chirp Group Delay-Based Onset Detection in Instruments with Fast Attack

Joysingh¹,

Vijayalakshmi²,

Nagarajan

2022

Circuits Syst Signal Process

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show abstract

“…Speech and music files were provided by D. Ellis, and recorded by E. Scheirer and M. Slaney [16]. This database [17] has already been used in a number of different works [16,[18][19][20]. Speech was recorded by digitally sampling FM radio stations, using a variety of stations, content styles and levels, and contains samples from both male and female speakers.…”

Section: Experimental Workmentioning

confidence: 99%

Analysis of the Effects of Finite Precision in Neural Network-Based Sound Classifiers for Digital Hearing Aids

Gil-Pita

Alexandre

Cuadra

et al. 2009

EURASIP J. Adv. Signal Process.

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The feasible implementation of signal processing techniques on hearing aids is constrained by the finite precision required to represent numbers and by the limited number of instructions per second to implement the algorithms on the digital signal processor the hearing aid is based on. This adversely limits the design of a neural network-based classifier embedded in the hearing aid. Aiming at helping the processor achieve accurate enough results, and in the effort of reducing the number of instructions per second, this paper focuses on exploring (1) the most appropriate quantization scheme and (2) the most adequate approximations for the activation function. The experimental work proves that the quantized, approximated, neural network-based classifier achieves the same efficiency as that reached by "exact" networks (without these approximations), but, this is the crucial point, with the added advantage of extremely reducing the computational cost on the digital signal processor.

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“…Both speech and music files were provided by D. Ellis, and were recorded by Scheirer and Slaney [39]. This database [40] has already been used by different authors in a number of works [39,[41][42][43].…”

Section: The Original "Feedback-free" Sound Databasementioning

confidence: 99%

Influence of Acoustic Feedback on the Learning Strategies of Neural Network-Based Sound Classifiers in Digital Hearing Aids

Cuadra

Alexandre

Gil-Pita

et al. 2009

EURASIP J. Adv. Signal Process.

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Sound classifiers embedded in digital hearing aids are usually designed by using sound databases that do not include the distortions associated to the feedback that often occurs when these devices have to work at high gain and low gain margin to oscillation. The consequence is that the classifier learns inappropriate sound patterns. In this paper we explore the feasibility of using different sound databases (generated according to 18 configurations of real patients), and a variety of learning strategies for neural networks in the effort of reducing the probability of erroneous classification. The experimental work basically points out that the proposed methods assist the neural network-based classifier in reducing its error probability in more than 18%. This helps enhance the elderly user's comfort: the hearing aid automatically selects, with higher success probability, the program that is best adapted to the changing acoustic environment the user is facing.

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A Speech-Music Discriminator Using HILN Model Based Features

Cited by 9 publications

References 9 publications

Chirp Group Delay-Based Onset Detection in Instruments with Fast Attack

Chirp Group Delay-Based Onset Detection in Instruments with Fast Attack

Analysis of the Effects of Finite Precision in Neural Network-Based Sound Classifiers for Digital Hearing Aids

Influence of Acoustic Feedback on the Learning Strategies of Neural Network-Based Sound Classifiers in Digital Hearing Aids

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