Adaptive Feature Selection for Speech / Music Classification

Abu-El-Quran, A.R.; Goubran, Rafik; Chan, Adrian D. C.

doi:10.1109/mmsp.2006.285299

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Various classifiers have been utilized for audio classification. Audio content analysis at Microsoft research commonly uses Gaussian mixture models (GMM) [29], k-nearest neighborhood (K-NN) [30] and support vector machine (SVM) [31] for audio classification. Other popular classifiers for audio classification include linear discriminant analysis (LDA) [32], hidden Markov models (HMM) [33] and artificial neural networks (ANN) [59].…”

Section: Audio Classifiersmentioning

confidence: 99%

Software-hardware analysis of signal feature classification algorithms

Asefi-Ghamari¹

2021

Preprint

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Over the last few decades, signal feature analysis has been significantly used in a wide variety of fields. While several techniques have been proposed in the area of signal feature extraction and classification, all of these techniques are achieved by using modern computers, which rely on softwares, such as MATLAB. However, in real-time applications or portable devices, software implementation is not enough by itself, and a hardware-software co-design or fully hardware implementation needs to be considered. The selection of the right signal feature analysis tool for an application depends not only on the software performance, but also on the hardware efficiency of a method. However, there is not enough studies in existence to provide comparison of these signal feature extraction methods from the hardware implentation aspect. Therefore, the objective of this thesis is to investigate both the hardware and algorithmic perspectives of three commonly used signal feature extraction techniques: Autoregressive (AR), pole modeling, and Mel-frequency Cepstral coefficients (MFCCs). To fulfill this objective, first, the hardware analysis of AR, pole modeling, and MFCC feature extraction methods is performed by calculating the computational complexity of the mathematical equations of each method. Second the FPGA area usage of each feature extraction methods is estimated. Third, algorithmic evaluation of these three methods is performed for audio scene analysis. Once the results are obtained from the above stages, the overall performance of each feature extraction method is compared in terms of both the hardware analysis and algorithmic performances. Finally, based on the performed comparison, pole modeling feature extraction approach is proposed as the suitable method for the audio scene analysis application. The suitable method (pole modeling feature extraction) + linear discriminant analysis (LDA) classifier are implemented in Altera DE2 Board using Altera Nios II soft-core processor. The audio classification accuracy obtained using this implementation is achieved to be equal to the MATLAB implementation. The classification time for one audio sample is determined to be 0.1s, which is fast enough to be considered as a real-time system for audio scene analysis application.

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Section: Audio Classifiersmentioning

confidence: 99%

Software-hardware analysis of signal feature classification algorithms

Asefi-Ghamari¹

2021

Preprint

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“…Various classifiers have been utilized for audio classification. Audio content analysis at Microsoft research commonly uses Gaussian mixture models (GMM) [122], k-nearest neighborhood (K-NN) [123] and support vector machine (SVM) [124] for audio classification. Other popular classifiers for audio classification include linear discriminant analysis (LDA) [125], hidden Markov models (HMM) [126] and artificial neural networks (ANN) [127].…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Feature Analysis

Ghoraani¹

2021

Preprint

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Most of the real-world signals in nature are non-stationary, i.e., their statistics are time variant. Extracting the time-varying frequency characteristics of a signal is very important in understanding the signal better, which could be of immense use in various applications such as pattern recognition and automated-decision making systems. In order to extract meaningful time-frequency (TF) features, a joint TF analysis is required. The proposed work is an attempt to develop a generalized TF analysis methodology that exploits the benefits of TF distribution (TFD) in pattern classification systems as related to discriminant feature detection and classification. Our objective is to introduce a unique and efficient way of performing non-stationary signal analysis using adaptive and discriminant TF techniques. To fulfill this objective, in the first point, we build a novel TF matrix (TFM) decomposition that increases the effectiveness of segmentation in real-world signals. Instantaneous and unique features are extracted from each segment such that they successfully represent joint TF structure of the signal. In the second point, based on the above technique, two unique and novel discriminant TF analysis methods are proposed to perform an improved and discriminant feature selection of any non-stationary signals. The first approach is a new machine learning method that identifies the clusters of the discriminant features to compute the presence of the discriminative pattern in any given signal, and classify them accordingly. The second approach is a discriminant TFM (DTFM) framework, which is a combination of TFM decomposition and the discriminant clustering techniques. The developed DTFM analysis automatically identifies the differences between different classes as the distinguishing structure, and uses the identified structure to accurately classify and locate the discriminant structure in the signal. The theoretical properties of the proposed approaches pertaining to pattern recognition and detection are examined in this dissertation. The extracted TF features provide strong and successful characterization and classification of real and synthetic non-stationary signals. The proposed TF techniques facilitate the adaptation of TF quantification to any feature detection technique in automating the identification process of discriminatory TF features, and can find applications in many different fields including biomedical and multimedia signal processing.

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“…Quran et al [8] proposed an audio classification system that employs an adaptive feature selection algorithm. Their system improves classification accuracy in low signal-to-noise ratios (SNR) by selecting the features that perform best on that SNR value.…”

Section: Introductionmentioning

confidence: 99%

A Speech/Music/Silence/Garbage/ Classifier for Searching and Indexing Broadcast News Material

Patsis

Verhelst

2008

2008 19th International Conference on Database and Expert Systems Applications

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An audio classifier that can distinguish between speech, music, silence and garbage has been developed. The classifier was trained and tested on broadcast news material provided by VRT (Flemish Radio and Television Network). Several feature sets and machine learning algorithms have been tested, providing choices of speed and performance for a target system. The audio classifier is part of a greater system that together with visual data can retrieve information from news broadcasts: speech can be converted to text and the speaker can be recognized. Music can be further used for genre classification, jingle recognition or copyright infringement detection. Silence is recognized and used to provide cues on topic changes or speaker turns. At this point everything that is not classified as speech, music or silence is labeled garbage. Garbage classes can be further used for background categorization giving information on the environment where someone speaks (an anchor in the studio or a reporter in the street).

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Adaptive Feature Selection for Speech / Music Classification

Cited by 6 publications

References 14 publications

Software-hardware analysis of signal feature classification algorithms

Software-hardware analysis of signal feature classification algorithms

Time-Frequency Feature Analysis

A Speech/Music/Silence/Garbage/ Classifier for Searching and Indexing Broadcast News Material

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