Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving certain types of music from large music data set. Classification is a fundamental problem in MIR. Many tasks in MIR can be naturally cast in a classification setting, such as genre classification, mood classification, artist recognition, instrument recognition, etc. Music annotation, a new research area in MIR that has attracted much attention in recent years, is also a classification problem in the general sense. Due to the importance of music classification in MIR research, rapid development of new methods, and lack of review papers on recent progress of the field, we provide a comprehensive review on audio-based classification in this paper and systematically summarize the state-of-the-art techniques for music classification. Specifically, we have stressed the difference in the features and the types of classifiers used for different classification tasks. This survey emphasizes on recent development of the techniques and discusses several open issues for future research.

A Survey of Audio-Based Music Classification and Annotation

Automatic identification of bird species by their vocalization is studied in this paper. Bird sounds are represented with two different parametric representations: (i) the mel-cepstrum parameters and (ii) a set of low-level signal parameters, both of which have been found useful for bird species recognition. Recognition is performed in a decision tree with support vector machine (SVM) classifiers at each node that perform classification between two species. Recognition is tested with two sets of bird species whose recognition has been previously tested with alternative methods. Recognition results with the proposed method suggest better or equal performance when compared to existing reference methods.

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Bird species recognition using support vector machines

The automatic recognition of sound events by computers is an important aspect of emerging applications such as automated surveillance, machine hearing and auditory scene understanding. Recent advances in machine learning, as well as in computational models of the human auditory system, have contributed to advances in this increasingly popular research field. Robust sound event classification, the ability to recognise sounds under real-world noisy conditions, is an especially challenging task. Classification methods translated from the speech recognition domain, using features such as mel-frequency cepstral coefficients, have been shown to perform reasonably well for the sound event classification task, although spectrogram-based or auditory image analysis techniques reportedly achieve superior performance in noise. This paper outlines a sound event classification framework that compares auditory image front end features with spectrogram image-based front end features, using support vector machine and deep neural network classifiers. Performance is evaluated on a standard robust classification task in different levels of corrupting noise, and with several system enhancements, and shown to compare very well with current state-of-the-art classification techniques.

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Robust sound event classification using deep neural networks

Despite surveillance systems becoming increasingly ubiquitous in our living environment, automated surveillance, currently based on video sensory modality and machine intelligence, lacks most of the time the robustness and reliability required in several real applications. To tackle this issue, audio sensory devices have been incorporated, both alone or in combination with video, giving birth in the past decade, to a considerable amount of research. In this article, audio-based automated surveillance methods are organized into a comprehensive survey: A general taxonomy, inspired by the more widespread video surveillance field, is proposed to systematically describe the methods covering background subtraction, event classification, object tracking, and situation analysis. For each of these tasks, all the significant works are reviewed, detailing their pros and cons and the context for which they have been proposed. Moreover, a specific section is devoted to audio features, discussing their expressiveness and their employment in the above-described tasks. Differing from other surveys on audio processing and analysis, the present one is specifically targeted to automated surveillance, highlighting the target applications of each described method and providing the reader with a systematic and schematic view useful for retrieving the most suited algorithms for each specific requirement.

Audio Surveillance: A Systematic Review

In the age of digital information, audio data has become an important part in many modern computer applications Audio classification has been becoming a focus in the research of audio processing and pattern recognition Automatic audio classification is very useful to audio indexing, content-based audio retrieval and on-line audio distribution, but it is a challenge to extract the most common and salient themes from unstructured raw audio data In this paper, we propose effective algorithms to automatically classify audio clips into one of six classes: music, news, sports, advertisement, cartoon and movie For these categories a number of acoustic features that include linear predictive coefficients, linear predictive cepstral coefficients and mel-frequency cepstral coefficients are extracted to characterize the audio content Support vector machines are applied to classify audio into their respective classes by learning from training data Then the proposed method extends the application of neural network (RBFNN) for the classification of audio RBFNN enables nonlinear transformation followed by linear transformation to achieve a higher dimension in the hidden space The experiments on different genres of the various categories illustrate the results of classification are significant and effective

Classification of audio signals using SVM and RBFNN

In this paper, an improved audio classification and categorization technique is presented. This technique makes use of wavelets and support vector machines (SVMs) to accurately classify and categorize audio data. When a query audio is given, wavelets are first applied to extract acoustical features such as subband power and pitch information. Then, the proposed method uses a bottom-up SVM over these acoustical features and additional parameters, such as frequency cepstral coefficients, to accomplish audio classification and categorization. A public audio database (Muscle Fish), which consists of 410 sounds in 16 classes, is used to evaluate the performances of the proposed method against other similar schemes. Experimental results show that the classification errors are reduced from 16 (8.1%) to six (3.0%), and the categorization accuracy of a given audio sound can achieve 100% in the Top 2 matches.

Audio classification and categorization based on wavelets and support vector Machine

This paper presents a scheme which combines the cubic spline interpolation (CSI) with standard H.264 codec in order to further improve the compression ratio at the same video quality. The CSI function is first applied to down-sample video frames to one quarter of the original video frames. The standard H.264 is then applied on those down-sampled frames to complete the encoding process. One can reconstruct the video clip back in reverse manner. Experimental results show that the proposed scheme can reduce bit-rate to 57%. Also, the proposed scheme may further speed the compression process up to almost 3 times faster than the standard H.264 codec at similar video qualities.

Very Low Bit Rate Video Coding Using H.264 Codec and Cubic Spline Interpolation

This paper proposes an improved DCT-based image codec embedded cubic spline interpolation (CSI) with optimal quantization. It is shown in literature that CSI is superior in performance to the other interpolation functions. One of main applications of the CSI is to cooperate with standard DCT-based JPEG to obtain the modified JPEG codec and still obtain a better quality of the reconstructed image for higher compression ratios. This paper shows that the compression performance of such a modified JPEG codec can be further improved by the use of optimal quantization. The optimal quantization makes use of the rate distortion optimization algorithm to measure DCT coefficient statistic for an image generated from the CSI scheme and then construct a DCT quantization table for the given rate/distortion specification. Experimental results show that the proposed optimal quantization can achieve a better PSNR than that of the modified JPEG codec with a default quantization table.

DCT-Based Image Codec Embedded Cubic Spline Interpolation with Optimal Quantization

In this paper, a modified image compression algorithm using cubic spline interpolation (CSI) and bit-plane compensation
is presented for low bit-rate transmission. The CSI is developed in order to subsample image data with minimal
distortion and to achieve image compression. It has been shown in literatures that the CSI can be combined with the
JPEG or JPEG2000 algorithm to develop a modified JPEG or JPEG2000 CODEC, which obtains a higher compression
ratio and better quality of reconstructed images than the standard JPEG and JPEG2000 CODECs in low bit-rate range.
This paper implements the modified JPEG algorithm, applies bit-plane compensation and tests a few images.
Experimental results show that the proposed scheme can increase 25~30% compression ratio of original JPEG data
compression system with similar visual quality in low bit-rate range. This system can reduce the loading of
telecommunication networks and is quite suitable for low bit-rate transmission.

Still image compression using cubic spline interpolation with bit-plane compensation

In this paper, there are 17 types of wavelet coefficients obtained from the Matlab software and an Aurora-2 database used to evaluate which wavelet type has a better accuracy in speaker recognition. We first determine the frequency cepstral coefficient (FCC) level to form a 114-dimentional feature vector by the use of Daubechies-4 wavelet and support vector machines (SVMs) with pre-selected exponential radial basis kernel function (ERBF) and under some additional conditions. Then, average, for each wavelet, the accuracy of 42 possible combinations about the gender of speakers considered in seven kinds of experiments corresponding to two to eight speakers. The experimental results show that the best accuracy in average will be achieved by using the reverse biorthogonal-3.5 or reverse biorthogonal-3.7 wavelet. The reverse biorthogonal-3.5 wavelet is then chosen to be the proposed wavelet function for speaker recognition in terms of shorter filter length.

Chien-Chang Lin

Papers

Audio classification and categorization based on wavelets and support vector Machine

Very Low Bit Rate Video Coding Using H.264 Codec and Cubic Spline Interpolation

DCT-Based Image Codec Embedded Cubic Spline Interpolation with Optimal Quantization

Still image compression using cubic spline interpolation with bit-plane compensation

Feature Comparison among Various Wavelets in Speaker Recognition Using Support Vector Machine