Acoustic Emotion Recognition Using Linear and Nonlinear Cepstral Coefficients
نویسندگان
چکیده
منابع مشابه
Acoustic Emotion Recognition Using Linear and Nonlinear Cepstral Coefficients
Recognizing human emotions through vocal channel has gained increased attention recently. In this paper, we study how used features, and classifiers impact recognition accuracy of emotions present in speech. Four emotional states are considered for classification of emotions from speech in this work. For this aim, features are extracted from audio characteristics of emotional speech using Linea...
متن کاملSpeech Emotion Recognition Based on Power Normalized Cepstral Coefficients in Noisy Conditions
Automatic recognition of speech emotional states in noisy conditions has become an important research topic in the emotional speech recognition area, in recent years. This paper considers the recognition of emotional states via speech in real environments. For this task, we employ the power normalized cepstral coefficients (PNCC) in a speech emotion recognition system. We investigate its perfor...
متن کاملMusical instrument recognition using cepstral coefficients and temporal features
In this paper, a system for pitch independent musical instrument recognition is presented. A wide set of features covering both spectral and temporal properties of sounds was investigated, and their extraction algorithms were designed. The usefulness of the features was validated using test data that consisted of 1498 samples covering the full pitch ranges of 30 orchestral instruments from the ...
متن کاملUsing Blob Detection in Missing Feature Linear-Frequency Cepstral Coefficients for Robust Sound Event Recognition
The proposed Missing Feature Linear-Frequency Cepstral Coefficients (MF-LFCC) is a noise robust cepstral feature that transforms both clean and noisy signals into a similar representation. Unlike conventional Missing Feature Techniques, the MF-LFCC does not require the substitution of spectrogram elements (imputation) or classifier modification (marginalization). To improve the noise mask used ...
متن کاملEmotion Recognition using Acoustic and Lexical Features
In this paper we present an innovative approach for utterance-level emotion recognition by fusing acoustic features with lexical features extracted from automatic speech recognition (ASR) output. The acoustic features are generated by combining: (1) a novel set of features that are derived from segmental Mel Frequency Cepstral Coefficients (MFCC) scored against emotion-dependent Gaussian mixtur...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Advanced Computer Science and Applications
سال: 2015
ISSN: 2156-5570,2158-107X
DOI: 10.14569/ijacsa.2015.061119