In this paper, we apply a discriminative weight training to a support vector machine (SVM) based gender identification. In our approach, the gender decision rule is derived by the SVM incorporating the optimally weighted mel-frequency cepstral coefficient (MFCC) based on a minimum classification error (MCE) method which is different from the previous works in that optimal weights are differentl...

We examined variants of MFCC and PLP cepstral parameterisations in the context of large vocabulary continuous speech recognition under di erent acoustical environmental conditions: Compared to MFCC, mel-frequency PLP uses a cubic root intensity-toloudness law, and an LPC analysis is applied to the mel-warped spectrum. In LPC-smoothed MFCC, the only di erence to MFCC is the additional LPC smooth...

Mel Frequency Cepstral Coefficient (MFCC) features are widely used as acoustic features for speech recognition as well as speaker recognition. In MFCC feature representation, the Mel frequency scale is used to get a high resolution in low frequency region, and a low resolution in high frequency region. This kind of processing is good for obtaining stable phonetic information, but not suitable f...

In this paper we study low-variance multi-taper spectrum estimation methods to compute the mel-frequency cepstral coefficient (MFCC) features for robust speech recognition. In speech recognition, MFCC features are usually computed from a Hamming-windowed DFT spectrum. Although windowing helps in reducing the bias of the spectrum, but variance remains high. Multitaper spectrum estimation methods...

Penelitian ini bertujuan untuk membandingkan akurasi pengenalan emosi melalui suara dengan menggunakan beberapa jenis classifier. Emosi dasar yang akan dikenali ada 4, yaitu senang, sedih, neutral dan marah. Metodologi penelitian dimulai memperoleh dataset dari database RAVDESS, terdiri 24 aktor jumlah sebanyak 60 per aktor. Namun, hanya 28 dipilih setiap aktor, sehingga total 672 digunakan dal...

This paper suggests Digital Signal processor (DSP) based speech recognition system with improved performance in terms of recognition accuracies and computational cost. The comprehensive surrey of various approaches of feature extraction like Mel filter banks with Mel Frequency Cepstrum Coefficients (MFCC). This paper describes an approach of isolated speech recognition by Digital Signal Process...

The report proposes a method for detecting the sound events in a basketball game with focusing on detecting cheering sound. MFCC (Mel-frequency cepstral coefficient) features are used to identify the cheering sounds from speeches and other confusing sounds. The mfcc features are fed into a neural network and classified into three classes (cheering, speech, and others). To improve the MFCC-NN pe...

Vocal and nonvocal segmentation is an important task in singing voice signal processing. Before identifying the singer it is necessary to locate the singer’s voice in a song. Maximum of the songs start with a piece of instrumental accompaniment known as ‘prelude’ in musical terms after which the singing voice comes into play. Therefore, it is necessary to detect the vocal region in the song in ...

Music genre classification has a significant role in information retrieval for the organization of growing collections music. It is challenging to classify music with reliable accuracy. Many methods have utilized handcrafted features identify unique patterns but are still unable determine original characteristics. Comparatively, using deep learning models been shown be dynamic and effective. Am...

The Mel-frequency cepstral coefficients (MFCC) are most widely used and successful features for speech recognition. But, their performance degrades in presence of additive noise. In this paper, we propose a noise compensation method for Mel filter bank energies and so MFCC features. This compensation method includes two steps: Mel sub-band spectral subtraction and then compression of Mel-Sub-ba...