Audio classification utilizing a rule-based approach and the support vector machine classifier

Vavrek, Jozef; Juhar, Jozef; Čižmár, Anton

doi:10.1109/tsp.2013.6613985

Cited by 5 publications

(2 citation statements)

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“…length of silent segment 300ms. We evaluated the silence detection accuracy also for VAD based on Variance of Acceleration MFC coefficients (VAMFCC) [8], in order to assess the effectiveness of PCAbased VAD. The threshold for VAMFCC parameter was changing in range 0.001; 0.01 for length of floating window 100ms, 300ms and 500ms.…”

Section: Methodsmentioning

confidence: 99%

Query-by-example retrieval via fast sequential dynamic time warping algorithm

Vavrek

Viszlay

Kiktová

et al. 2015

2015 38th International Conference on Telecommunications and Signal Processing (TSP)

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We introduce a novel approach to Query-byExample (QbE) retrieval, utilizing fundamental principles of posteriorgram-based Spoken Term Detection (STD), in this paper. Proposed approach is a kind of modification of widely used segmental variant of dynamic programming algorithm. Our solution represents sequential variant of DTW algorithm, employing one step forward moving strategy. Each DTW search is carried out sequentially, block by block, where each block represents squared input distance matrix, with size equal to the length of retrieved query. We also examine a way how to speed up sequential DTW algorithm without considerable loss in retrieving performance, by implementing linear time-aligned accumulated distance. The increase of detection accuracy is ensured by weighted cumulative distance score parameter. Therefore, we called this approach Weighted Fast Sequential -DTW (WFS-DTW) algorithm. A novel PCA-based silence discriminator is used along with this algorithm. Evaluation of proposed algorithm is carried out on ParDat1 corpus, using Term Weighted Value (TWV).

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Section: Methodsmentioning

confidence: 99%

Query-by-example retrieval via fast sequential dynamic time warping algorithm

Vavrek

Viszlay

Kiktová

et al. 2015

2015 38th International Conference on Telecommunications and Signal Processing (TSP)

Self Cite

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“…Especially, this discrimination is accomplished by the common platform of a support vector machine (SVM) classifier. 25) 2. Proposed SVM-based capacitive TSP Three kinds of capacitive TSP systems including finger-only touch, conventional active stylus-touch, and proposed active stylus-touch are depicted in Fig.…”

Section: Introductionmentioning

confidence: 99%

Finger and stylus discrimination scheme based on capacitive touch screen panel and support vector machine classifier

Seol

Park

Song

et al. 2019

Jpn. J. Appl. Phys.

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This paper demonstrates a support vector machine (SVM) based capacitive touch screen scheme that can discriminate stylus and finger at the same time. The tip of a stylus provides pulses at the higher frequency than the transmitting (Tx) pulse of a touch screen. Then the digital value acquired from an analog-to-digital converter is transferred to an SVM classifier to make the decision of which touch is applied among no-touch, finger-touch, and stylus-touch. Three types of touches are processed on the SVM algorithm. The proposed method is evaluated by means of an 8 inch capacitive touch panel, connector board, Tx/Rx driver board, and host processor board. While Tx pulses are applied at 5 V and 32 kHz that lead to the 200 Hz reporting rate, stylus pulses are produced at 3 V and 315 kHz. The resultant bit error rate is measured as less than 10−6 for all types of touches.

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Audio Genre Classification Employing Support Vector Machine

Mali¹,

Mahajan²

2018

2018 4th International Conference for Convergence in Technology (I2CT)

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Audio classification utilizing a rule-based approach and the support vector machine classifier

Cited by 5 publications

References 5 publications

Query-by-example retrieval via fast sequential dynamic time warping algorithm

Query-by-example retrieval via fast sequential dynamic time warping algorithm

Finger and stylus discrimination scheme based on capacitive touch screen panel and support vector machine classifier

Audio Genre Classification Employing Support Vector Machine

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