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

High-quality speech modification based on a harmonic + noise model.

A method and apparatus for synchronizing audio to an asynchronous clock while preserving pitch utilizes a phase-vocoder to implement time-scaling without pitch-shifting.

Pitch-Preserved digital audio playback synchronized to asynchronous clock

The improvement of identification accuracy of concurrent vowels with differences in fundamental frequency (ΔF0) is usually attributed to mechanisms that exploit harmonic structure. To decide whether identification is aided primarily by selecting the target vowel on the basis of its harmonic structure (‘‘harmonic enhancement’’) or removing the interfering vowel on the basis of its harmonic structure (‘‘harmonic cancellation’’), pairs of synthetic vowels, each of which was either harmonic or inharmonic, were presented to listeners for identification. Responses for each vowel were scored according to the vowel’s harmonicity and that of the vowel that accompanied it. For a given target, identification was better by about 3% for a harmonic ground unless the target was also harmonic with the same F0. This supports the cancellation hypothesis. Identification was worse for harmonic than for inharmonic targets by 3%–8%. This does not support the enhancement hypothesis. When both vowels were harmonic, identificatio...

/pdf/identification-of-concurrent-harmonic-and-inharmonic-vowels-46k1chm121.pdf

Identification of concurrent harmonic and inharmonic vowels: A test of the theory of harmonic cancellation and enhancement

The problem of estimating the tempo of audio recordings (the number of beats per minute, or BPM) has received an increasing amount of attention in the past few years. Applications include the synchronization of multiple audio tracks for simultaneous playback, "tempo-synchronous" audio effects, automatic looping of audio tracks etc. This article presents techniques for estimating the tempo and the swing, and locating the beats in audio recordings, under the assumption that the tempo is constant. The techniques rely on a preliminary transient detection stage where note onsets/offsets, percussion hits and other time-localized events are detected. This first step is followed by a maximum likelihood estimation of the tempo, swing and downbeat. Suggestions are given to minimize the computation load of the methods.

/pdf/estimating-tempo-swing-and-beat-locations-in-audio-4rdj7b2qnj.pdf

Estimating tempo, swing and beat locations in audio recordings

In the context of spectrum analysis, high‐resolution estimation methods present interesting alternatives to classical Fourier transform. These methods are based on the assumption that the analyzed signal is composed of damped sinusoids, and yield estimates of their parameters: frequencies, damping factors, original amplitudes, and phases. This paper investigates the use of a specific high‐resolution estimation method, the matrix‐pencil method, for the spectrum analysis of musical signals. In spite of its computational cost, this technique is shown to achieve extremely good results in the case of highly damped signals, short data records, or close frequencies.

Jean Laroche

Papers

High-quality speech modification based on a harmonic + noise model.

Pitch-Preserved digital audio playback synchronized to asynchronous clock

Identification of concurrent harmonic and inharmonic vowels: A test of the theory of harmonic cancellation and enhancement

Estimating tempo, swing and beat locations in audio recordings

The use of the matrix pencil method for the spectrum analysis of musical signals