scholarly journals Speech/music classification using PLP and SVM

2019 ◽  
Vol 8 (02) ◽  
pp. 24469-24472
Author(s):  
Thiruven Gatanadhan R
Keyword(s):  
Feature Extraction ◽  
Linear Prediction ◽  
Classification Problem ◽  
Vector Model ◽  
Support Vector ◽  
Audio Classification ◽  
Music Classification ◽  
Audio Retrieval ◽  
Audio Indexing ◽  
Perceptual Linear Prediction

Automatic audio classification is very useful in audio indexing; content based audio retrieval and online audio distribution. This paper deals with the Speech/Music classification problem, starting from a set of features extracted directly from audio data. Automatic audio classification is very useful in audio indexing; content based audio retrieval and online audio distribution. The accuracy of the classification relies on the strength of the features and classification scheme. In this work Perceptual Linear Prediction (PLP) features are extracted from the input signal. After feature extraction, classification is carried out, using Support Vector Model (SVM) model. The proposed feature extraction and classification models results in better accuracy in speech/music classification.

Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition

2016 ◽  
Vol 7 (1) ◽  
pp. 58-68 ◽  
Author(s):  
Imen Trabelsi ◽  
Med Salim Bouhlel
Keyword(s):  
Emotion Recognition ◽  
Linear Prediction ◽  
Recognition Rate ◽  
Gaussian Mixture ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Emotional States ◽  
Wide Range ◽  
Leibler Divergence ◽  
Perceptual Linear Prediction

Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients (MFCC), Linear prediction coefficients (LPC), linear prediction cepstrum coefficients (LPCC), Perceptual Linear Prediction (PLP) and Relative Spectral Perceptual Linear Prediction (Rasta-PLP) features are used to characterize the emotional utterances using a combination between Gaussian mixture models (GMM) and Support Vector Machines (SVM) based on the Kullback-Leibler Divergence Kernel. In this study, the effect of feature type and its dimension are comparatively investigated. The best results are obtained with 12-coefficient MFCC. Utilizing the proposed features a recognition rate of 84% has been achieved which is close to the performance of humans on this database.

Discriminative Training Using Noise Robust Integrated Features and Refined HMM Modeling

2018 ◽  
Vol 29 (1) ◽  
pp. 327-344 ◽  
Author(s):  
Mohit Dua ◽  
Rajesh Kumar Aggarwal ◽  
Mantosh Biswas
Keyword(s):  
Feature Extraction ◽  
Linear Prediction ◽  
Extraction Methods ◽  
Discriminative Training ◽  
Mel Frequency Cepstral Coefficients ◽  
Maximum Mutual Information ◽  
Perceptual Linear Prediction ◽  
Noise Robust ◽  
Minimum Phone Error ◽  
Asr System

Abstract The classical approach to build an automatic speech recognition (ASR) system uses different feature extraction methods at the front end and various parameter classification techniques at the back end. The Mel-frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) techniques are the conventional approaches used for many years for feature extraction, and the hidden Markov model (HMM) has been the most obvious selection for feature classification. However, the performance of MFCC-HMM and PLP-HMM-based ASR system degrades in real-time environments. The proposed work discusses the implementation of discriminatively trained Hindi ASR system using noise robust integrated features and refined HMM model. It sequentially combines MFCC with PLP and MFCC with gammatone-frequency cepstral coefficient (GFCC) to obtain MF-PLP and MF-GFCC integrated feature vectors, respectively. The HMM parameters are refined using genetic algorithm (GA) and particle swarm optimization (PSO). Discriminative training of acoustic model using maximum mutual information (MMI) and minimum phone error (MPE) is preformed to enhance the accuracy of the proposed system. The results show that discriminative training using MPE with MF-GFCC integrated feature vector and PSO-HMM parameter refinement gives significantly better results than the other implemented techniques.

scholarly journals Spectrogram Classification Using Dissimilarity Space

2020 ◽  
Vol 10 (12) ◽  
pp. 4176 ◽  
Author(s):  
Loris Nanni ◽  
Andrea Rigo ◽  
Alessandra Lumini ◽  
Sheryl Brahnam
Keyword(s):  
Ad Hoc ◽  
Classification Problem ◽  
Space Representation ◽  
Support Vector ◽  
Clustering Methods ◽  
Audio Classification ◽  
Classification Problems ◽  
Clustering Techniques ◽  
Vector Space Representation ◽  
Better Than

In this work, we combine a Siamese neural network and different clustering techniques to generate a dissimilarity space that is then used to train an SVM for automated animal audio classification. The animal audio datasets used are (i) birds and (ii) cat sounds, which are freely available. We exploit different clustering methods to reduce the spectrograms in the dataset to a number of centroids that are used to generate the dissimilarity space through the Siamese network. Once computed, we use the dissimilarity space to generate a vector space representation of each pattern, which is then fed into an support vector machine (SVM) to classify a spectrogram by its dissimilarity vector. Our study shows that the proposed approach based on dissimilarity space performs well on both classification problems without ad-hoc optimization of the clustering methods. Moreover, results show that the fusion of CNN-based approaches applied to the animal audio classification problem works better than the stand-alone CNNs.

scholarly journals Automatic Content based Classification of Speech Audio using Multiple Instance Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 410-414
Keyword(s):  
Linear Prediction ◽  
Research Problem ◽  
Multiple Instance Learning ◽  
Audio Classification ◽  
Mel Frequency Cepstral Coefficients ◽  
Content Understanding ◽  
Novel Approach ◽  
Active Research ◽  
Perceptual Linear Prediction

Audio content understanding is an active research problem in the area of speech analytics. A novel approach for content-based news audio classification using Multiple Instance Learning (MIL) approach is introduced in this paper. Content-based analysis provides useful information for audio classification as well as segmentation. A key step taken in this direction is to propose a classifier that can predict the category of the input audio sample. There are two types of features used for audio content detection, namely, Perceptual Linear Prediction (PLP) coefficients and Mel-Frequency Cepstral Coefficients (MFCC). Two MIL techniques viz. mi-Graph and mi-SVM are used for classification purpose. The results obtained using these methods are evaluated using different performance matrices. From the experimental results, it is marked that the MIL demonstrates excellent audio classification capability.

scholarly journals Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhao Zhang ◽  
Wang Li ◽  
Yuyang Zhang
Keyword(s):  
Artificial Intelligence ◽  
Feature Extraction ◽  
Classification Problem ◽  
Human Motion ◽  
Small Samples ◽  
Support Vector ◽  
Convolutional Network ◽  
Multilabel Classification ◽  
Practical Applications ◽  
Automatic Construction

In this paper, we study the automatic construction and extraction of feature variables of sports moments and construct the extraction of the specific variables by artificial intelligence. In this paper, support vector machines, which have better performance in the case of small samples, are selected as classifiers, and multiclass classifiers are constructed in a one-to-one manner to achieve the classification and recognition of human sports postures. The classifier for a single decomposed action is constructed to transform the automatic description problem of free gymnastic movements into a multilabel classification problem. With the increase in the depth of the feature extraction network, the experimental effect is enhanced; however, the two-dimensional convolutional neural network loses temporal information when extracting features, so the three-dimensional convolutional network is used in this paper for spatial-temporal feature extraction of the video. The extracted features are binary classified several times to achieve the goal of multilabel classification. To form a comparison experiment, the results of the classification are randomly combined into a sentence and compared with the results of the automatic description method to verify the effectiveness of the method. The multiclass classifier constructed in this paper is used for human motion pose classification and recognition tests, and the experimental results show that the human motion pose recognition algorithm based on multifeature fusion can effectively improve the recognition accuracy and perform well in practical applications.

scholarly journals Genetic Algorithm for Combined Speaker and Speech Recognition using Deep Neural Networks

2018 ◽  
Vol 2 ◽  
pp. 23-31 ◽  
Author(s):  
Gurpreet Kaur ◽  
Mohit Srivastava ◽  
Amod Kumar
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Speech Recognition ◽  
Speaker Recognition ◽  
Linear Prediction ◽  
Rate Sensitivity ◽  
Second Phase ◽  
Linear Predictive Coding ◽  
Mel Frequency Cepstral Coefficients ◽  
Perceptual Linear Prediction

Huge growth is observed in the speech and speaker recognition field due to many artificial intelligence algorithms being applied. Speech is used to convey messages via the language being spoken, emotions, gender and speaker identity. Many real applications in healthcare are based upon speech and speaker recognition, e.g. a voice-controlled wheelchair helps control the chair. In this paper, we use a genetic algorithm (GA) for combined speaker and speech recognition, relying on optimized Mel Frequency Cepstral Coefficient (MFCC) speech features, and classification is performed using a Deep Neural Network (DNN). In the first phase, feature extraction using MFCC is executed. Then, feature optimization is performed using GA. In the second phase training is conducted using DNN. Evaluation and validation of the proposed work model is done by setting a real environment, and efficiency is calculated on the basis of such parameters as accuracy, precision rate, recall rate, sensitivity, and specificity. Also, this paper presents an evaluation of such feature extraction methods as linear predictive coding coefficient (LPCC), perceptual linear prediction (PLP), mel frequency cepstral coefficients (MFCC) and relative spectra filtering (RASTA), with all of them used for combined speaker and speech recognition systems. A comparison of different methods based on existing techniques for both clean and noisy environments is made as well.

scholarly journals Feature Extraction Methods Proposed for Speech Recognition Are Effective on Road Condition Monitoring Using Smartphone Inertial Sensors

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3481 ◽  
Author(s):  
Frederico Soares Cabral ◽  
Hidekazu Fukai ◽  
Satoshi Tamura
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Condition Monitoring ◽  
Linear Prediction ◽  
Machine Learning Techniques ◽  
Feature Extraction Method ◽  
Extraction Step ◽  
Road Condition ◽  
Road Condition Monitoring ◽  
Perceptual Linear Prediction

The objective of our project is to develop an automatic survey system for road condition monitoring using smartphone devices. One of the main tasks of our project is the classification of paved and unpaved roads. Assuming recordings will be archived by using various types of vehicle suspension system and speeds in practice, hence, we use the multiple sensors found in smartphones and state-of-the-art machine learning techniques for signal processing. Despite usually not being paid much attention, the results of the classification are dependent on the feature extraction step. Therefore, we have to carefully choose not only the classification method but also the feature extraction method and their parameters. Simple statistics-based features are most commonly used to extract road surface information from acceleration data. In this study, we evaluated the mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction coefficients (PLP) as a feature extraction step to improve the accuracy for paved and unpaved road classification. Although both MFCC and PLP have been developed in the human speech recognition field, we found that modified MFCC and PLP can be used to improve the commonly used statistical method.

Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition

2020 ◽  
pp. 283-293
Author(s):  
Imen Trabelsi ◽  
Med Salim Bouhlel
Keyword(s):  
Emotion Recognition ◽  
Linear Prediction ◽  
Recognition Rate ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Emotional States ◽  
Perceptual Linear Prediction

Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients (MFCC), Linear prediction coefficients (LPC), linear prediction cepstrum coefficients (LPCC), Perceptual Linear Prediction (PLP) and Relative Spectral Perceptual Linear Prediction (Rasta-PLP) features are used to characterize the emotional utterances using a combination between Gaussian mixture models (GMM) and Support Vector Machines (SVM) based on the Kullback-Leibler Divergence Kernel. In this study, the effect of feature type and its dimension are comparatively investigated. The best results are obtained with 12-coefficient MFCC. Utilizing the proposed features a recognition rate of 84% has been achieved which is close to the performance of humans on this database.

Feature Selection for GUMI Kernel-Based SVM in Speech Emotion Recognition

2015 ◽  
Vol 6 (2) ◽  
pp. 57-68 ◽  
Author(s):  
Imen Trabelsi ◽  
Med Salim Bouhlel
Keyword(s):  
Emotion Recognition ◽  
Linear Prediction ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Bhattacharyya Distance ◽  
Human Machine Interaction ◽  
Speech Database ◽  
Perceptual Linear Prediction

Speech emotion recognition is the indispensable requirement for efficient human machine interaction. Most modern automatic speech emotion recognition systems use Gaussian mixture models (GMM) and Support Vector Machines (SVM). GMM are known for their performance and scalability in the spectral modeling while SVM are known for their discriminatory power. A GMM-supervector characterizes an emotional style by the GMM parameters (mean vectors, covariance matrices, and mixture weights). GMM-supervector SVM benefits from both GMM and SVM frameworks. In this paper, the GMM-UBM mean interval (GUMI) kernel based on the Bhattacharyya distance is successfully used. CFSSubsetEval combined with Best first algorithm and Greedy stepwise were also utilized on the supervectors space in order to select the most important features. This framework is illustrated using Mel-frequency cepstral (MFCC) coefficients and Perceptual Linear Prediction (PLP) features on two different emotional databases namely the Surrey Audio-Expressed Emotion and the Berlin Emotional speech Database.

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