Musical genres are categorical labels created by humans to characterize pieces of music. A musical genre is characterized by the common characteristics shared by its members. These characteristics typically are related to the instrumentation, rhythmic structure, and harmonic content of the music. Genre hierarchies are commonly used to structure the large collections of music available on the Web. Currently musical genre annotation is performed manually. Automatic musical genre classification can assist or replace the human user in this process and would be a valuable addition to music information retrieval systems. In addition, automatic musical genre classification provides a framework for developing and evaluating features for any type of content-based analysis of musical signals. In this paper, the automatic classification of audio signals into an hierarchy of musical genres is explored. More specifically, three feature sets for representing timbral texture, rhythmic content and pitch content are proposed. The performance and relative importance of the proposed features is investigated by training statistical pattern recognition classifiers using real-world audio collections. Both whole file and real-time frame-based classification schemes are described. Using the proposed feature sets, classification of 61% for ten musical genres is achieved. This result is comparable to results reported for human musical genre classification.

/pdf/musical-genre-classification-of-audio-signals-4c1fbmnwe0.pdf

Musical genre classification of audio signals

Digital audio, video, images, and documents are flying through cyberspace to their respective owners. Unfortunately, along the way, individuals may choose to intervene and take this content for themselves. Digital watermarking and steganography technology greatly reduces the instances of this by limiting or eliminating the ability of third parties to decipher the content that he has taken. The many techiniques of digital watermarking (embedding a code) and steganography (hiding information) continue to evolve as applications that necessitate them do the same. The authors of this second edition provide an update on the framework for applying these techniques that they provided researchers and professionals in the first well-received edition. Steganography and steganalysis (the art of detecting hidden information) have been added to a robust treatment of digital watermarking, as many in each field research and deal with the other. New material includes watermarking with side information, QIM, and dirty-paper codes. The revision and inclusion of new material by these influential authors has created a must-own book for anyone in this profession.

*This new edition now contains essential information on steganalysis and steganography
*New concepts and new applications including QIM introduced
*Digital watermark embedding is given a complete update with new processes and applications

Digital Watermarking and Steganography

https://wiki.inf.ed.ac.uk/twiki/pub/CSTR/Speak08To09/STRAIGHT-original.pdf

Restructuring speech representations using a pitch-adaptive time-frequency smoothing and an instantaneous-frequency-based F0 extraction: possible role of a repetitive structure in sounds

Voice conversion, as considered in this paper, is defined as modifying the speech signal of one speaker (source speaker) so that it sounds as if it had been pronounced by a different speaker (target speaker). Our contribution includes the design of a new methodology for representing the relationship between two sets of spectral envelopes. The proposed method is based on the use of a Gaussian mixture model of the source speaker spectral envelopes. The conversion itself is represented by a continuous parametric function which takes into account the probabilistic classification provided by the mixture model. The parameters of the conversion function are estimated by least squares optimization on the training data. This conversion method is implemented in the context of the HNM (harmonic+noise model) system, which allows high-quality modifications of speech signals. Compared to earlier methods based on vector quantization, the proposed conversion scheme results in a much better match between the converted envelopes and the target envelopes. Evaluation by objective tests and formal listening tests shows that the proposed transform greatly improves the quality and naturalness of the converted speech signals compared with previous proposed conversion methods.

Continuous probabilistic transform for voice conversion

There has been a great deal of material in the area of discrete-time transforms that has been published in recent years. This book does an excellent job of presenting important aspects of such material in a clear manner. The book has 11 chapters and a very useful appendix. Seven of these chapters are essentially devoted to the Fourier series/transform, discrete Fourier transform, fast Fourier transform (FFT), and applications of the FFT in the area of spectral estimation. Chapters 8 through 10 deal with many other discrete-time transforms and algorithms to compute them. Of these transforms, the KarhunenLoeve, the discrete cosine, and the Walsh-Hadamard transform are perhaps the most well-known. A lucid discussion of number theoretic transforms i5 presented in Chapter 11. This reviewer feels that the authors have done a fine job of compiling the pertinent material and presenting it in a concise and clear manner. There are a number of problems at the end of each chapter, an appreciable number of which are challenging. The authors have included a comprehensive set of references at the end of the book. In brief, this book is a valuable contribution to the general areas of signal processing and communications. It can be used for a graduate level course in perhaps two ways. One would be to cover the first seven chapters in great detail. The other would be to cover the whole book by focussing on different topics in a selective manner. This book  by  Elliott  and Rao  is extremely  useful to researchers/engineers who are working  in the areas of signal processing and communications. It i s also an excellent reference book, and hence a valuable addition to one’s library

http://ieeexplore.ieee.org/iel5/5/31347/01457444.pdf

Multirate digital signal processing

The present technology provides techniques for transform domain reconstruction of noise-corrupted portions of an acoustic signal to emulate speech which is obscured by the noise Replacement transform values for the noise-corrupted portions are determined utilizing the portions of the acoustic signal which contain speech

Spectrum reconstruction for automatic speech recognition

The problem of the bounded-input/bounded-output stability of time-varying recursive filters is discussed. While simple, well-known criteria exist for the stability of time-invariant filters, guaranteeing stability when the filter coefficients are allowed to vary is much more difficult. Better insight into the causes for instability can be gained by using the state-space representation of the filter and examining the singular values of the state transition matrix. Two simple criteria based on the state transition matrix can be derived that guarantee the stability of the time-varying filter. Moreover, in the second-order case the singular values of this matrix provide a useful estimate of the maximum and average signal gains that result from the modification of the filter coefficients. These estimates can be used in practice to keep a time-varying filter from blowing up. It is also shown that some filter topologies are better suited to time-varying filtering than others, and a few techniques are presented that can be used to stabilize an otherwise unstable time-varying filter.

On the Stability of Time-Varying Recursive Filters

A frequency band of an audio input signal is analyzed to determine if a transient is present. When transients are detected, modifications are made to the intensity levels corresponding to the frequency band for a brief time period.

Method for enhancing audio signals

Techniques for synthesizing a time-domain signal. The time-domain signal is partitioned into a number of time-domain frames and a waveform in generated for each time-domain frame. Each waveform includes one or more sinusoids. The waveform is generated by selecting a sinusoid for synthesis and computing a set of parameter values (e.g. the start and end amplitude, frequency, and phase values) for the selected sinusoid. A template is determined for the selected sinusoid based on the computed parameter values and a selected window function. The frequency-domain template is such that the amplitude of the selected sinusoid in the time domain matches, at a time-domain frame boundary, the amplitude of a corresponding sinusoid in an adjacent time-domain frame. The template is added to a frequency-domain frame. The process is repeated for each sinusoid in the waveform. After all sinusoids have been processed, the frequency-domain frame is transformed to a time-domain frame. The time-domain frame is re-normalized with a re-normalization function that is generated based on the selected window function. A predetermined number of samples from each end of the time-domain frame can be discarded. The waveform is defined by the non-discarded samples in the time-domain frame. The waveforms from the time-domain frames are concatenated to generate the time-domain signal.

Synthesis of time-domain signals using non-overlapping transforms

We consider the problem of segmenting an audio signal into characteristic regions based on feature-set similarities. In the proposed approach, a feature-space representation of the signal is generated; sequences of these feature-space samples are then aggregated into clusters corresponding to distinct signal regions. The algorithm consists of using linear discriminant analysis (LDA) to condition the feature space and dynamic programming (DP) to identify data clusters. We consider the design of the dynamic program cost functions; we are able to derive effective cost functions without relying on significant prior information about the structure of the expected data clusters. We demonstrate the application of the LDA-DP segmentation algorithm to speech/music discrimination. Experimental results are given and discussed.

Jean Laroche

Papers

Spectrum reconstruction for automatic speech recognition

On the Stability of Time-Varying Recursive Filters

Method for enhancing audio signals

Synthesis of time-domain signals using non-overlapping transforms

A dynamic programming approach to audio segmentation and speech/music discrimination