• Journal of information and communication convergence engineering
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• v.5 no.3
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• pp.245-248
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• 2007

In this paper, we research the content-based analysis and classification according to the composition of the feature parameter data base for the audio data to implement the audio data index and searching system. Audio data is classified to the primitive various auditory types. We described the analysis and feature extraction method for the feature parameters available to the audio data classification. And we compose the feature parameters data base in the index group unit, then compare and analyze the audio data centering the including level around and index criterion into the audio categories. Based on this result, we compose feature vectors of audio data according to the classification categories, and simulate to classify using discrimination function.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.97-104
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• 2007

This paper presents the problem of the feature extraction techniques that has been used a content-based analysis, classification and retrieval in audio data and proposes a course of the preprocessing for a new contents-based retrieval methods. Because the feature vector according to sampling value changes, the existing audio data analysis is problem that same music is appraised by other music. Therefore, we propose waveform information extraction method of PCM data for retrieval audio data of various format to contents-based. If this method is used. we can find that audio datas that get into sampling in various format are same data. And it may be applied in contents-based music retrieval system. To verity the performance of the method, an experiment was done feature extraction using STFT and waveform information extraction using PCM data. As a result, we could know that the method to propose is effective more.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.353-362
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• 2005

Recently, study of Bimodal recognition has become very active. In this paper we propose a Bimodal user recognition system that uses face information and audio information. Face recognition consists of face detection step and face recognition step. Face detection uses AdaBoost to find face candidate area. After finding face candidates, PCA feature extraction is applied to decrease the dimension of feature vector. And then, SVM classifiers are used to detect and recognize face. Audio recognition uses MFCC for audio feature extraction and HMM is used for audio recognition. Experimental results show that the Bimodal recognition can improve the user recognition rate much more than audio only recognition, especially in the Presence of noise.

• The Journal of the Acoustical Society of Korea
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• v.23 no.3E
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• pp.91-97
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• 2004

In this paper, we propose a classification system of TV program scenes based on audio information. The system classifies the video scene into six categories of commercials, basketball games, football games, news reports, weather forecasts and music videos. Two type of audio feature set are extracted from each audio frame-timbral features and coefficient domain features which result in 58-dimensional feature vector. In order to reduce the computational complexity of the system, 58-dimensional feature set is further optimized to yield l0-dimensional features through Sequential Forward Selection (SFS) method. This down-sized feature set is finally used to train and classify the given TV program scenes using κ -NN, Gaussian pattern matching algorithm. The classification result of 91.6% reported here shows the promising performance of the video scene classification based on the audio information. Finally, the system stability problem corresponding to different query length is investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.6038-6053
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• 2017

In this study, we propose an automatic melody extraction algorithm using deep learning. In this algorithm, feature images, generated using the energy of frequency band, are extracted from polyphonic audio files and a deep learning technique, a convolutional neural network (CNN), is applied on the feature images. In the training data, a short frame of polyphonic music is labeled as a musical note and a classifier based on CNN is learned in order to determine a pitch value of a short frame of audio signal. We want to build a novel structure of melody extraction, thus the proposed algorithm has a simple structure and instead of using various signal processing techniques for melody extraction, we use only a CNN to find a melody from a polyphonic audio. Despite of simple structure, the promising results are obtained in the experiments. Compared with state-of-the-art algorithms, the proposed algorithm did not give the best result, but comparable results were obtained and we believe they could be improved with the appropriate training data. In this paper, melody extraction and the proposed algorithm are introduced first, and the proposed algorithm is then further explained in detail. Finally, we present our experiment and the comparison of results follows.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.390-395
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• 2009

The robustness of audio fingerprinting system in a noisy environment is a principal challenge in the area of content-based audio retrieval. The selected feature for the audio fingerprints must be robust in a noisy environment and the computational complexity of the searching algorithm must be low enough to be executed in real-time. The audio fingerprint proposed by Philips uses expanded hash table lookup to compensate errors introduced by noise. The expanded hash table lookup increases the searching complexity by a factor of 33 times the degree of expansion defined by the hamming distance. We propose a new method to improve noise robustness of audio fingerprinting in noise environment using predominant pitch which reduces the bit error of created hash values. The sub-fingerprint of our approach method is computed in each time frames of audio. The time frame is transformed into the frequency domain using FFT. The obtained audio spectrum is divided into 33 critical bands. Finally, the 32-bit hash value is computed by difference of each bands of energy. And only store bits near predominant pitch. Predominant pitches are extracted in each time frames of audio. The extraction process consists of harmonic enhancement, harmonic summation and selecting a band among critical bands.

• Journal of the Korea Society of Computer and Information
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• v.14 no.8
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• pp.51-58
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• 2009

In this paper. we propose search algorithm using Low Peak Feature of audio and image color value by which similar movies can be identified. Combing through entire video files for the purpose of recognizing and retrieving matching movies requires much time and memory space. Moreover, these methods still share a critical problem of erroneously recognizing as being different matching videos that have been altered only in resolution or converted merely with a different codec. Thus we present here a similar-video-retrieval method that relies on analysis of audio patterns, whose peak features are not greatly affected by changes in the resolution or codec used and image color values. which are used for similarity comparison. The method showed a 97.7% search success rate, given a set of 2,000 video files whose audio-bit-rate had been altered or were purposefully written in a different codec.

• MALSORI
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• no.61
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• pp.63-74
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• 2007

Discrimination of music and speech from the multimedia signal is an important task in audio coding and broadcast monitoring systems. This paper deals with the problem of feature parameter extraction for discrimination of music and speech. The wavelet transform is a multi-resolution analysis method that is useful for analysis of temporal and spectral properties of non-stationary signals such as speech and audio signals. We propose new feature parameters extracted from the wavelet transformed signal for discrimination of music and speech. First, wavelet coefficients are obtained on the frame-by-frame basis. The analysis frame size is set to 20 ms. A parameter $E_{sum}$ is then defined by adding the difference of magnitude between adjacent wavelet coefficients in each scale. The maximum and minimum values of $E_{sum}$ for period of 2 seconds, which corresponds to the discrimination duration, are used as feature parameters for discrimination of music and speech. To evaluate the performance of the proposed feature parameters for music and speech discrimination, the accuracy of music and speech discrimination is measured for various types of music and speech signals. In the experiment every 2-second data is discriminated as music or speech, and about 93% of music and speech segments have been successfully detected.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.201-204
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• 2007

In this paper, we research the content-based analysis and classification according to the composition of the feature parameters pool for the auditory signals to implement the auditory indexing and searching system. Auditory data is classified to the primitive various auditory types. we described the analysis and feature extraction method for the feature parameters available to the auditory data classification. And we compose the feature parameters pool in the indexing group unit, then compare and analysis the auditory data centering around the including level and indexing criterion into the audio categories. Based on this result, we composit feature vectors of audio data according to the classification categories, then experiment the classification using discrimination function.

• The Journal of the Acoustical Society of Korea
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• v.39 no.3
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• pp.143-149
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• 2020

This paper presents the effect of the feature extraction methods used in the audio preprocessing on the classification performance of the Convolutional Neural Networks (CNN). We extract mel spectrogram, log mel spectrogram, Mel Frequency Cepstral Coefficient (MFCC), and delta MFCC from the UrbanSound8K dataset, which is widely used in environmental sound classification studies. Then we scale the data to 3 distributions. Using the data, we test four CNNs, VGG16, and MobileNetV2 networks for performance assessment according to the audio features and scaling. The highest recognition rate is achieved when using the unscaled log mel spectrum as the audio features. Although this result is not appropriate for all audio recognition problems but is useful for classifying the environmental sounds included in the Urbansound8K.