Audio indexing including frequency tracking of simultaneous multiple sources in speech and music

Coz, Maxime Le; Pinquier, Julien; André-Obrecht, Régine; Mauclair, Julie

doi:10.1109/cbmi.2013.6576547

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…In [7], [8], the discrimination of an audio stream into different audio types has been performed using Support Vector Machine (SVM) algorithm and K-Nearest Neighbor (KNN) algorithm. Moreover, the characterization of various audio content levels of a sound track has been carried out by frequency tracking in an audio indexing system proposed in [9]. This system has the specificity that it does not need any prior information.…”

Section: Introductionmentioning

confidence: 99%

An Optimization of Audio Classification and Segmentation using GASOM Algorithm

Karim¹,

Cherif²,

Salah³

2018

ijacsa

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Now-a-days, multimedia content analysis occupies an important place in widely used applications. It may depend on audio segmentation which is one of the many other tools used in this area. In this paper, we present an optimized audio classification and segmentation algorithms that are used to segment a superimposed audio stream according to its content into 10 main audio types: speech, non-speech, silence, male speech, female speech, music, environmental sounds, and music genres, such as classic music, jazz, and electronic music. We have tested the KNN, SVM, and GASOM algorithms on two audio classification systems. In the first audio classification system, the audio stream is discriminated into speech no-speech, purespeech/silence, male speech/female speech, and music/ environmental sounds. However, in the second audio classification system, the audio stream is segmented into music/speech, pure-speech/silence, male speech/female speech. For pure-speech/silence discrimination, it is performed in the two systems according to a rule-based classifier. Concerning the music segments in both systems, they are discriminated into different music genres using the decision tree as a classifier. Also, the first audio classification system has succeeded to achieve higher performances compared to the second one. Indeed, in the first system using the GASOM algorithm with leave-one-out validation technique, the average accuracy has reached 99.17% for the music/environmental sounds discrimination. Moreover, in both systems, the GASOM algorithm has always reached the best results of performances compared to KNN and SVM algorithms. Therefore, in the first system, the GASOM algorithm has been contributed to obtain an optimized consumption time compared to that one obtained using the two HMM and MLP methods.

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Section: Introductionmentioning

confidence: 99%

An Optimization of Audio Classification and Segmentation using GASOM Algorithm

Karim¹,

Cherif²,

Salah³

2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…Coz et al [10] presented an audio indexing system that characterizes various content levels of a sound track by frequency tracking. The system does not require any prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

Optimized Audio Classification and Segmentation Algorithm by Using Ensemble Methods

Zahid

Hussain

Rashid

et al. 2015

Mathematical Problems in Engineering

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Audio segmentation is a basis for multimedia content analysis which is the most important and widely used application nowadays. An optimized audio classification and segmentation algorithm is presented in this paper that segments a superimposed audio stream on the basis of its content into four main audio types: pure-speech, music, environment sound, and silence. An algorithm is proposed that preserves important audio content and reduces the misclassification rate without using large amount of training data, which handles noise and is suitable for use for real-time applications. Noise in an audio stream is segmented out as environment sound. A hybrid classification approach is used, bagged support vector machines (SVMs) with artificial neural networks (ANNs). Audio stream is classified, firstly, into speech and nonspeech segment by using bagged support vector machines; nonspeech segment is further classified into music and environment sound by using artificial neural networks and lastly, speech segment is classified into silence and pure-speech segments on the basis of rule-based classifier. Minimum data is used for training classifier; ensemble methods are used for minimizing misclassification rate and approximately 98% accurate segments are obtained. A fast and efficient algorithm is designed that can be used with real-time multimedia applications.

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“…In [39], each peak trajectory is independently tracked and the harmonic series are constructed afterwards by grouping the appropriated trajectories. In [37], [28], constraints of harmonicity are used during the tracking operation; however, only one or two harmonic series can be retrieved.…”

Section: B Tracking Of Harmonic Seriesmentioning

confidence: 99%

Time-Frequency Tracking of Spectral Structures Estimated by a Data-Driven Method

Gerber

Martin

Mailhes

2015

IEEE Trans. Ind. Electron.

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Abstract-The installation of a condition monitoring system aims to reduce the operating costs of the monitored system by applying a predictive maintenance strategy. However, a systemdriven configuration of the condition monitoring system requires the knowledge of the system kinematics and could induce lots a false alarms because of predefined thresholds. The purpose of this paper is to propose a complete data-driven method to automatically generate system health indicators without any a priori on the monitored system or the acquired signals. This method is composed of two steps. First, every acquired signal is analysed: the spectral peaks are detected and then grouped in more complex structure as harmonic series or modulation sidebands. Then, a time-frequency tracking operation is applied on all available signals: the spectral peaks and the spectral structures are tracked over time and grouped in trajectories, which will be used to generate the system health indicators. The proposed method is tested on real-world signals coming from a wind turbine test rig. The detection of a harmonic series and a modulation sideband reports the birth of a fault on the main bearing inner ring. The evolution of the fault severity is characterised by three automatically generated health indicators and is confirmed by experts.

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Audio indexing including frequency tracking of simultaneous multiple sources in speech and music

Cited by 4 publications

References 9 publications

An Optimization of Audio Classification and Segmentation using GASOM Algorithm

An Optimization of Audio Classification and Segmentation using GASOM Algorithm

Optimized Audio Classification and Segmentation Algorithm by Using Ensemble Methods

Time-Frequency Tracking of Spectral Structures Estimated by a Data-Driven Method

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