An Extensive Analysis of Query by Singing/Humming System Through Query Proportion

Nagavi, Trisiladevi C.

doi:10.5121/ijma.2012.4606

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…An audio feature extraction and a multi-group classification scheme that focuses on identifying discriminatory timefrequency subspaces using the Local Discriminate Bases (LDB) technique has been described in (Mishra and Agrawal, 2012). For pure music and vocal music, a num-ber of features such as LPC and LPCC are extracted in (Nagavi and Bhajantri, 2012) to characterize the music content. Based on calculated features, a clustering algorithm is applied to structure the music content.…”

Section: Related Workmentioning

confidence: 99%

Speech/Music Classification Using Wavelet Based Feature Extraction Techniques

Ramalingam¹,

Dhanalakshmi²

2014

Journal of Computer Science

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Audio classification serves as the fundamental step towards the rapid growth in audio data volume. Due to the increasing size of the multimedia sources speech and music classification is one of the most important issues for multimedia information retrieval. In this work a speech/music discrimination system is developed which utilizes the Discrete Wavelet Transform (DWT) as the acoustic feature. Multi resolution analysis is the most significant statistical way to extract the features from the input signal and in this study, a method is deployed to model the extracted wavelet feature. Support Vector Machines (SVM) are based on the principle of structural risk minimization. SVM is applied to classify audio into their classes namely speech and music, by learning from training data. Then the proposed method extends the application of Gaussian Mixture Models (GMM) to estimate the probability density function using maximum likelihood decision methods. The system shows significant results with an accuracy of 94.5%.

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Section: Related Workmentioning

confidence: 99%

Speech/Music Classification Using Wavelet Based Feature Extraction Techniques

Ramalingam¹,

Dhanalakshmi²

2014

Journal of Computer Science

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“…Starting from two decades in the past, specific techniques such as Query by Humming have been periodically summarized [20,44,73], thus testifying an evolution in the discipline.…”

Section: Motivations and Related Workmentioning

confidence: 99%

“…Concerning similarity measurements, several distance metrics have been evaluated [44] in QbH scenario: Euclidean Distance (ED), Dynamic Time Warping (DTW), and KNearest Neighbour (k-NN). Each technique has been applied in turn to MFCCs, Linear Predictive Coefficients (LPCs) and Linear Predictive Cepstral Coefficients (LPCCs) to assess which combination would produce the best retrieval accuracy.…”

Section: Matching Strategiesmentioning

confidence: 99%

Non-speech voice for sonic interaction: a catalogue

Piccolo

Rocchesso

2016

J Multimodal User Interfaces

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This paper surveys the uses of non-speech voice as an interaction modality within sonic applications. Three main contexts of use have been identified: sound retrieval, sound synthesis and control, and sound design. An overview of different choices and techniques regarding the style of interaction, the selection of vocal features and their mapping to sound features or controls is here displayed. A comprehensive collection of examples instantiates the use of non-speech voice in actual tools for sonic interaction. It is pointed out that while voice-based techniques are already being used proficiently in sound retrieval and sound synthesis, their use in sound design is still at an exploratory phase. An example of creation of a voice-driven sound design tool is here illustrated.

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