Automatic Music Transcription: An Overview <s> Analogies to other fields <s> We investigate the problem of modeling symbolic sequences of polyphonic music in a completely general piano-roll representation. We introduce a probabilistic model based on distribution estimators conditioned on a recurrent neural network that is able to discover temporal dependencies in high-dimensional sequences. Our approach outperforms many traditional models of polyphonic music on a variety of realistic datasets. We show how our musical language model can serve as a symbolic prior to improve the accuracy of polyphonic transcription. <s> BIB001 </s> Automatic Music Transcription: An Overview <s> Analogies to other fields <s> This book presents computational methods for extracting the useful information from audio signals, collecting the state of the art in the field of sound event and scene analysis. The authors cover the entire procedure for developing such methods, ranging from data acquisition and labeling, through the design of taxonomies used in the systems, to signal processing methods for feature extraction and machine learning methods for sound recognition. The book also covers advanced techniques for dealing with environmental variation and multiple overlapping sound sources, and taking advantage of multiple microphones or other modalities. The book gives examples of usage scenarios in large media databases, acoustic monitoring, bioacoustics, and context-aware devices. Graphical illustrations of sound signals and their spectrographic representations are presented, as well as block diagrams and pseudocode of algorithms. ::: Gives an overview of methods for computational analysis of sounds scenes and events, allowing those new to the field to become fully informed; ::: Covers all the aspects of the machine learning approach to computational analysis of sound scenes and events, ranging from data capture and labeling process to development of algorithms; ::: Includes descriptions of algorithms accompanied by a website from which software implementations can be downloaded, facilitating practical interaction with the techniques. <s> BIB002
AMT has close relations with other signal processing problems. With respect to the field of speech processing, AMT is widely considered to be the musical equivalent of automatic speech recognition (ASR), in the sense that both tasks involve converting acoustic signals to symbolic sequences. Like the cocktail party problem in speech, music usually involves multiple simultaneous voices, but, unlike speech, these voices are highly correlated in time and in frequency (see challenges 2 and 3 in the "Key Challenges" section). In addition, both AMT and ASR systems benefit from language modeling components that are combined with acoustic components to produce plausible results. Thus, there are also clear links between AMT and the wider field of natural language processing (NLP), with music having its own grammatical rules or statistical regularities, in a way similar to natural language BIB001 . The use of language models for AMT is detailed in the section "Further Extensions and Future Work." Within the emerging field of sound scene analysis, there is a direct analogy between AMT and sound event detection (SED) BIB002 , in particular with polyphonic SED, which involves detecting and classifying multiple overlapping events from audio. While everyday and natural sounds do not exhibit the same degree of temporal regularity and intersource frequency dependence as found in music signals, there are close interactions AMT is an enabling technology with clear potential for both economic and societal impact. between the two problems in terms of the methodologies used, as observed in the literature BIB002 . Furthermore, AMT is related to image processing and computer vision, as musical objects, such as notes, can be recognized as two-dimensional patterns in time-frequency representations. Compared with image processing and computer vision, where occlusion is a common issue, AMT systems are often affected by musical objects occupying the same time-frequency regions (this is detailed in the "Key Challenges" section).