Content based audio retrieval with MFCC feature extraction, clustering and sort-merge techniques

Nagavi, Trisiladevi C.; Anusha, S; Monisha, P.; Poornima, S.

doi:10.1109/icccnt.2013.6850234

Cited by 6 publications

(3 citation statements)

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“…The Mel-frequency cepstrum has proven to be highly effective in recognizing structure of music signals and in modeling the subjective pitch and frequency content of audio signals [3]. MFCCs are based on the known variation of the human ears critical bandwidths with frequency, filters spaced linearly at low frequencies and logarithmically at high frequencies to capture the phonetically important characteristics of speech and audio [4].…”

Section: Mel-frequency Cepstral Coefficientsmentioning

confidence: 99%

“…More precisely, for a given signal frame, it consists of a series of values, each one expressing the deviation of the signal's power spectrum from a flat shape inside a predefined frequency band. The first step of the ASF extraction is the calculation of the power spectrum of each signal frame as specified in Equation (3). In this case, the power coefficients P(k) are obtained from non-overlapping frames.…”

Section: Audio Spectrum Flatnessmentioning

confidence: 99%

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GMM Based Indexing and Retrieval of Music Using MFCC and MPEG-7 Features

Thiruvengatanadhan

Dhanalakshmi

Palanivel

2015

Advances in Intelligent Systems and Computing

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Abstract. Audio which includes voice, music, and various kinds of environmental sounds, is an important type of media, and also a significant part of video. The digital music databases in place these days, people begin to realize the importance of effectively managing music databases relying on music content analysis. The goal of music indexing and retrieval system is to provide the user with capabilities to index and retrieve the music data in an efficient manner. For efficient music retrieval, some sort of music similarity measure is desirable. In this paper, we propose a method for indexing and retrieval of the classified music using Mel-Frequency Cepstral Coefficients (MFCC) and MPEG-7 features. Music clip extraction, feature extraction, creation of an index and retrieval of the query clip are the major issues in automatic audio indexing and retrieval. Indexing is done for all the music audio clips using Gaussian mixture model (GMM) models, based on the features extracted. For retrieval, the probability that the query feature vector belongs to each of the Gaussian is computed. The average Probability density function is computed for each of the Gaussians and the retrieval is based on the highest probability.

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Section: Mel-frequency Cepstral Coefficientsmentioning

confidence: 99%

Section: Audio Spectrum Flatnessmentioning

confidence: 99%

GMM Based Indexing and Retrieval of Music Using MFCC and MPEG-7 Features

Thiruvengatanadhan

Dhanalakshmi

Palanivel

2015

Advances in Intelligent Systems and Computing

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“…As far as audio data is concerned, we compute the Mel Frequency Cepstral Coefficients (MFCCs) [73] corresponding to the sound of each footage segment. MFCCs have been successfully used for audio classification and retrieval schemes [74], [75] as they can represent the spectral properties of audio data in a compact fashion.…”

Section: Training Data Preparationmentioning

confidence: 99%

The Pixels and Sounds of Emotion: General-Purpose Representations of Arousal in Games

Makantasis,

Liapis,

Yannakakis

2021

Preprint

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What if emotion could be captured in a general and subject-agnostic fashion? Is it possible, for instance, to design general-purpose representations that detect affect solely from the pixels and audio of a human-computer interaction video? In this paper we address the above questions by evaluating the capacity of deep learned representations to predict affect by relying only on audiovisual information of videos. We assume that the pixels and audio of an interactive session embed the necessary information required to detect affect. We test our hypothesis in the domain of digital games and evaluate the degree to which deep classifiers and deep preference learning algorithms can learn to predict the arousal of players based only on the video footage of their gameplay. Our results from four dissimilar games suggest that general-purpose representations can be built across games as the arousal models obtain average accuracies as high as 85% using the challenging leave-one-video-out cross-validation scheme. The dissimilar audiovisual characteristics of the tested games showcase the strengths and limitations of the proposed method.

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Content-Based Audio Classification and Retrieval Using Segmentation, Feature Extraction and Neural Network Approach

Patil

Nemade

2019

Advances in Intelligent Systems and Computing

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Content based audio retrieval with MFCC feature extraction, clustering and sort-merge techniques

Cited by 6 publications

References 0 publications

GMM Based Indexing and Retrieval of Music Using MFCC and MPEG-7 Features

GMM Based Indexing and Retrieval of Music Using MFCC and MPEG-7 Features

The Pixels and Sounds of Emotion: General-Purpose Representations of Arousal in Games

Content-Based Audio Classification and Retrieval Using Segmentation, Feature Extraction and Neural Network Approach

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