Discrete-time signal processing
Discrete-time signal processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A General Framework of Progressive Filtering and Its Application to Query by Singing/Humming
IEEE Transactions on Audio, Speech, and Language Processing
Note segmentation and quantization for music information retrieval
IEEE Transactions on Audio, Speech, and Language Processing
Combining evidence from temporal and spectral features for person recognition using humming
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
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Automatic Speaker Recognition (ASR)is an economic method of biometrics because of the availability of low cost and powerful processors. An ASR system will be efficient if the proper speaker-specificfeatures are extracted. Most of the state-of-the-art ASR systems use the natural speech signal (either read speech or spontaneous speech) from the subjects. In this paper, an attempt is made to identify speakers from their hum. The experiments are shown for Linear Prediction Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), and Mel Frequency Cepstral Coefficients (MFCC) as input feature vectors to the polynomial classifier of 2ndorder approximation. Results are found to be better for MFCC than LP-based features.