This book serves as an ideal starting point for newcomers and an excellent reference source for people already working in the field. Researchers and graduate students in signal processing, computer science, acoustics and music will primarily benefit from this text. It could be used as a textbook for advanced courses in music signal processing. Since it only requires a basic knowledge of signal processing, it is accessible to undergraduate students.

When Speech and Audio Signal Processing published in 1999, it stood out from its competition in its breadth of coverage and its accessible, intutiont-based style. This book was aimed at individual students and engineers excited about the broad span of audio processing and curious to understand the available techniques. Since then, with the advent of the iPod in 2001, the field of digital audio and music has exploded, leading to a much greater interest in the technical aspects of audio processing. This Second Edition will update and revise the original book to augment it with new material describing both the enabling technologies of digital music distribution (most significantly the MP3) and a range of exciting new research areas in automatic music content processing (such as automatic transcription, music similarity, etc.) that have emerged in the past five years, driven by the digital music revolution. New chapter topics include: Psychoacoustic Audio Coding, describing MP3 and related audio coding schemes based on psychoacoustic masking of quantization noise Music Transcription, including automatically deriving notes, beats, and chords from music signals. Music Information Retrieval, primarily focusing on audio-based genre classification, artist/style identification, and similarity estimation. Audio Source Separation, including multi-microphone beamforming, blind source separation, and the perception-inspired techniques usually referred to as Computational Auditory Scene Analysis (CASA).

This dissertation addresses the problem of automatic music transcription. Transcription is an open problem in the area of musical analysis and audio signal processing, and one that has drawn increased attention in recent years. Despite the increase in related research, a system that is capable of transcription in the general sense does not appear to be on the horizon. In this document, we propose a new approach to the problem, motivated by the human approach to transcription, that is hoped to be a foundation for constructing a viable system in the future. Additionally, we investigate a method of onset and offset detection, based upon a model of human auditory physiology, for robustly identifying temporal boundaries of sound events while retaining information of benefit to subsequent stages of processing within the transcription framework.

Automatic music transcription (AMT) is a relatively new application in the field of music signal processing. The purpose of an AMT algorithm is to transform a raw acoustic musical signal into a written version, namely a score. The most basic pieces of information an AMT system aims to extract from a raw acoustic musical signal are the properties of individual note events, such as the starting time (onset), duration, and pitch. Because of its overwhelming complexity, the transcription problem has been broken down into sub-tasks, and separate algorithms have been developed over the years to address different operations in the overall system. Pitch detection is an important part of any transcription system, and has been the subject of a vast volume of research over the past two decades. Estimation of a single pitch at each time step is known as monophonic pitch detection. In this work, we present an instance-based classification approach to transcription of monophonic melodies. Depending on the size of training database, two different pitch classification methods are proposed. The conventional K-Nearest Neighbor algorithm is trained on a large database of piano notes and employed for pitch detection. A two-step algorithm, combining semi-KNN pitch candidate selection and note sequence tracking is suggested to deal with cases in which the training database is of minimum size, containing one sample per class. It is demonstrated that in the abundance of training data, the KNN algorithm along with a proper choice of the distance measure and K, yields high performance accuracy. Furthermore, while maintaining low computational complexity, the proposed two-step algorithm is capable of compensating for the shortage of data by incorporating prior musicological information in the transcription process. We note that monophonic pitch detection is a mature problem compared to polyphonic pitch detection, which is the main focus of current studies. Nevertheless, monophonic pitch detection can still be of interest since a considerable portion of music corpora is composed of single line melodies. One of the shortcomings of the available monophonic algorithms, which are mostly based on signal processing techniques such as autocorrelation function (ACF) or spectral peak picking, is that they are under-evaluated in terms of frequency range and melodic structure. Classification-based pitch detection algorithms have been proposed later on and particularly developed for polyphony. Despite their promising performance accuracy, the classification methods that have been employed to solve the multi-pitch detection problem, namely Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs), are computationally too demanding to be utilized for the monophonic case which is a simpler scenario. The work presented in this thesis was motivated by a need for monophonic transcription techniques with significantly reduced training time, low run-time complexity, and the capability to explore melodic contours.