In this paper our main aim to provide the difference between cepstral and non-cepstral feature extraction techniques. Here we try to cover-up most of the comparative features of Mel Frequency Cepstral Coefficient and prosodic features. In speaker recognition, there are two type of techniques are available for feature extraction: Short-term features i.e. Mel Frequency Cepstral Coefficient (MFCC)...

The purpose of this paper is to describe the work carried out for the Violent Scenes Detection task at MediaEval 2013 by team TUDCL. Our work is based on the combination of visual, temporal and audio features with machine learning at segment-level. Block-saliency-map based dense trajectory is proposed for visual and temporal features, and MFCC and delta-MFCC is used for audio features. For the ...

This paper proposes a Mel Frequency Cepstral Coefficient (MFCC) based hybrid algorithm for motor imagery classification of Electroencephalogram (EEG) signal for Brain Computer Interface (BCI). The proposed hybrid algorithm contains MFCC with Hjorth Parameter. Regression coefficient method was used for eye artifacts cancellation. The feature extraction method based on the difference of the diffe...

This full abstract describes our submitted system for the MIREX08 Audio Genre Classification task, the goal of which is to discriminate music excerpts of different genres/styles. The system is based on basic feature of MFCC and modeling framework of GSV-SVM, which has been successfully applied in speaker recognition field. In this submission, the only basic feature we use is MFCC. And the goal ...

Due to the increasing use of fusion in speaker recognition systems, one trend of current research activity focuses on new features that capture complementary information to the MFCC (Mel-frequency cepstral coefficients) for improving speaker recognition performance. The goal of this work is to combine (or fuse) amplitude and phase-based features to improve speaker verification performance. Base...

Speech synthesis and voice conversion techniques can pose threats to current speaker verification (SV) systems. For this purpose, it is essential to develop front end systems that are able to distinguish human speech vs. spoofed speech (synthesized or voice converted). In this paper, for the ASVspoof 2015 challenge, we propose a detector based on combination of cochlear filter cepstral coeffici...

Physiologically motivated feature extraction methods based on 2D-Gabor filters have already been used successfully in robust automatic speech recognition (ASR) systems. Recently it was shown that a Mel Frequency Cepstral Coefficients (MFCC) baseline can be improved with physiologically motivated features extracted by a 2D-Gabor filter bank (GBFB). Besides physiologically inspired approaches to ...

In this paper two popular feature extraction techniques Linear Predictive Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) have been investigated and their performances have been evaluated for the recognition of Assamese phonemes. A multilayer perceptron based baseline phoneme recognizer has been built and all the experiments have been carried out using that recognize...

The automatic speaker verification spoofing and countermeasures challenge 2015 provides a common framework for the evaluation of spoofing countermeasures or anti-spoofing techniques in the presence of various seen and unseen spoofing attacks. This contribution proposes a system consisting of amplitude, phase, linear prediction residual, and combined amplitude phase-based countermeasures for the...

Human beatboxing is a vocal art making use of speech organs to produce drum sounds and imitate musical instruments. Beatbox sound classification current challenge that can be used for automatic database annotation music-information retrieval. In this study, large-vocabulary human-beatbox recognition system was developed with an adaptation Kaldi toolbox, widely-used tool recognition. The corpus ...