Segmentation and characterization of acoustic event spectrograms using singular value decomposition
| Autor: | Manjunath Mulimani, Shashidhar G. Koolagudi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Feature vector General Engineering Pattern recognition 02 engineering and technology Speaker recognition Computer Science Applications Support vector machine Singular value Noise 020901 industrial engineering & automation Computer Science::Sound Artificial Intelligence Singular value decomposition 0202 electrical engineering electronic engineering information engineering Spectrogram 020201 artificial intelligence & image processing Mel-frequency cepstrum Artificial intelligence business |
| Zdroj: | Expert Systems with Applications. 120:413-425 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2018.12.004 |
| Popis: | The traditional frame-based speech features such as Mel-frequency cepstral coefficients (MFCCs) are specifically developed for speech/speaker recognition tasks. Speech is different from acoustic events, when one considers its phonetic structure. Hence, frame-based speech features may not be suitable for Acoustic Event Classification (AEC). In this paper, a novel method is proposed for the extraction of robust acoustic event specific features from the spectrogram using a left singular vector for AEC. It consists of two main stages: segmentation and characterization of acoustic event spectrograms. In the first stage, symmetric Laplacian matrix of an acoustic event spectrogram is decomposed into singular values and vectors. Then, reliable region (spectral shape) of an acoustic from the spectrogram is segmented using a left singular vector. The selected prominent values of a left singular vector using the proposed threshold, automatically segment the reliable region of an acoustic event from the spectrogram. In the second stage, the segmented region of the spectrogram is used as a feature vector for AEC. Characteristics of values of singular vector belonging to reliable (event) and unreliable (non-event) regions of the spectrogram are determined. To evaluate the proposed approach, different categories of ‘home’ acoustic events are considered from the Freiburg-106 dataset. The results show that the significantly improved performance of acoustic event segmentation and classification. A singular vector effectively segments the reliable region of the acoustic event from spectrogram for Support Vector Machine (SVM) based AEC system. The proposed AEC system is robust to noise and achieves higher recognition rate in clean and noisy conditions compared to the traditional speech feature based systems. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect