We evaluate a new filterbank structure, yielding the harmonic structure cepstral coefficients (HSCCs), on a mismatchedsession closed-set speaker classification task. The novelty of the filterbank lies in its averaging of energy at frequencies related by harmonicity rather than by adjacency. Improvements are presented which achieve a 37%rel reduction in error rate under these conditions. The imp...

identifying an unknown language from the test utterances. In this paper, a new method of feature extraction, viz., Teager Energy Based Mel Frequency Cepstral Coefficients (T-MFCC) is developed for identification of perceptually similar languages. Finally, an LID system is presented for Hindi and Urdu (perceptually similar Indian languages) to demonstrate effectiveness of newly proposed feature ...

We describe a perceptual space for timbre, define an objective metric that takes into account perceptual orthogonality and measure the quality of timbre interpolation. We discuss two timbre representations and measure perceptual judgments. We determine that a timbre space based on Mel-frequency cepstral coefficients (MFCC) is a good model for perceptual timbre space.

We describe a perceptual space for timbre, define an objective metric that takes into account perceptual orthogonality and measure the quality of timbre interpolation. We discuss two timbre representations and measure perceptual judgments on an equivalent range of timbre variety. We determine that a timbre space based on Mel-frequency cepstral coefficients (MFCC) is a good model for a perceptua...

In the analysis of musical acoustics, we usually use the power spectrum to describe the difference between timbres from two music instruments. However, according to our experiments, the power spectrum cannot be used as effective features for erhu instrument identification. In this paper, we use MFCC (mel-scale frequency cepstral coefficients) as features for music instrument identification usin...

Automatic Speech Recognition (ASR) involves mainly two steps; feature extraction and classification (pattern recognition). Mel Frequency Cepstral Coefficient (MFCC) is used as one of the prominent feature extraction techniques in ASR. Usually, the set of all 12 MFCC coefficients is used as the feature vector in the classification step. But the question is whether the same or improved classifica...

In the study, the effectiveness of combinations of cepstral features, channel compensation techniques, and different local distances in the Dynamic Time Warping (DTW) algorithm is experimentally evaluated in the text-dependent speaker identification task. The training and the testing has been done with noisy telephone speech (short phrases in Bulgarian with length of about 2 seconds) selected f...

Paper presents an comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in speaker dependent mode for 100 different Lithuanian words. The robus...

The goal of speech emotion recognition (SER) is to identify the emotional or physical state of a human being from his or her voice. One of the most important things in a SER task is to extract and select relevant speech features with which most emotions could be recognized. In this paper, we present a smoothed nonlinear energy operator (SNEO)-based amplitude modulation cepstral coefficients (AM...

Lots of work has been done on speech and speaker recognition. Many technologies were developed for the analysis of speech waveforms. Musical instrument recognition is an important aspect of music information retrieval system. In this paper we analyzed features for musical instruments recognition and a brief study on music similarity. Music similarity search is done by using mel-frequency cepstr...