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2 publications

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    Principal Component Analysis Based Dominant Features Selection Method for Speaker Identification

    By Md. Ekramul Hamid, Md. Khademul Islam Molla, Wade Ghribi

    Abstract: Cepstrum based features are mostly used in speaker identification. Mel-frequency cepstrum coefficients (MFCCs) and their statistical properties (skewness, kurtosis and standard deviation) are used in this paper for text-dependent speaker identification. Principal component analysis (PCA) is employed to select the dominant feature vector representing the speaker characteristics. Multi-layer neural network is used as the classification engine. There occurs the inter-speaker variation of speech length uttering the same word. The feature …

    Published in Current Trends in Signal Processing · Vol. 1, Issue 1-3, 2025 · pp. 33–45 Read article

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    Localization Based Beamforming Approach to Audio Source Separation from Binaural Mixtures

    By Md. Ekramul Hamid, Md. Khademul Islam Molla, Keikichi Hirose

    Abstract: This paper presents an approach of localization on the basis of audio source separation from binaural mixtures with adaptive beamforming. A new source localization technique is proposed to spatially localize the sources with two degrees of freedom (azimuth and elevation). We used a head related transfer function to synthesize the binaural mixture that introduces the interaural differences to the source signals. The interaural cues are technically employed to estimate the …

    Published in Current Trends in Signal Processing · Vol. 1, Issue 1-3, 2025 · pp. 1–10 Read article

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