Single channel speech enhancement using adaptive filtering and best correlating noise identification
| Autor: | M S Athulya, Sathidevi Puthumangalathu Savithri, Vinayshankar Somalara Nataraj |
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| Rok vydání: | 2017 |
| Předmět: |
Computer science
Speech recognition Noise reduction 050801 communication & media studies 02 engineering and technology Least mean squares filter symbols.namesake 0508 media and communications Signal-to-noise ratio 0202 electrical engineering electronic engineering information engineering Median filter Value noise Recursive least squares filter Noise measurement Noise (signal processing) business.industry 020208 electrical & electronic engineering 05 social sciences Pattern recognition Adaptive filter Gradient noise Speech enhancement Noise Computer Science::Sound Gaussian noise visual_art visual_art.visual_art_medium symbols Bark Artificial intelligence business |
| Zdroj: | CCECE |
| DOI: | 10.1109/ccece.2017.7946770 |
| Popis: | Speech enhancement using adaptive filtering methods are known to give good signal recovery from the noisy speech signal. Among these, Least Mean Square (LMS) and Recursive Least Squares (RLS) algorithms are more popular. These algorithms have a constraint that correlating noise should be given as the reference signal for denoising. Therefore in all the adaptive algorithms, two microphones are used, one for capturing noisy speech and the other for capturing noise signal alone. Always, capturing noise alone is difficult. To overcome the above constraint, we propose a novel method which identifies the best correlating part of the noise signal with respect to noise in noisy speech and this can be used as the reference for speech enhancement in adaptive algorithms. Once, best correlating noise is identified, Variable Step Size LMS (VSSLMS) and RLS algorithms are used for speech enhancement. Prior to this, noise classification is done to identify the type of noise present in the speech. Bark features and Support Vector Machine (SVM) are used for noise classification. SVM based noise classification combined with the identification of the best correlating part of noise for using as reference in adaptive algorithms for single channel speech enhancement is proposed in this work. Proposed system gives very good performance even in the case of speech mixed with non-stationary noise under very low signal-to-noise ratio (SNR) conditions. |
| Databáze: | OpenAIRE |
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