Automatic Music Transcription: An Overview <s> Applications and impact <s> Music signal processing may appear to be the junior relation of the large and mature field of speech signal processing, not least because many techniques and representations originally developed for speech have been applied to music, often with good results. However, music signals possess specific acoustic and structural characteristics that distinguish them from spoken language or other nonmusical signals. This paper provides an overview of some signal analysis techniques that specifically address musical dimensions such as melody, harmony, rhythm, and timbre. We will examine how particular characteristics of music signals impact and determine these techniques, and we highlight a number of novel music analysis and retrieval tasks that such processing makes possible. Our goal is to demonstrate that, to be successful, music audio signal processing techniques must be informed by a deep and thorough insight into the nature of music itself. <s> BIB001 </s> Automatic Music Transcription: An Overview <s> Applications and impact <s> We provide a survey of the field of Music Information Retrieval (MIR), in particular paying attention to latest developments, such as semantic auto-tagging and user-centric retrieval and recommendation approaches. We first elaborate on well-established and proven methods for feature extraction and music indexing, from both the audio signal and contextual data sources about music items, such as web pages or collaborative tags. These in turn enable a wide variety of music retrieval tasks, such as semantic music search or music identification ("query by example"). Subsequently, we review current work on user analysis and modeling in the context of music recommendation and retrieval, addressing the recent trend towards user-centric and adaptive approaches and systems. A discussion follows about the important aspect of how various MIR approaches to different problems are evaluated and compared. Eventually, a discussion about the major open challenges concludes the survey. <s> BIB002
A successful AMT system would enable a broad range of interactions between people and music, including music education (e.g., through systems for automatic instrument tutoring), music creation (e.g., dictating improvised musical ideas and automatic music accompaniment), music production (e.g., music content visualization and intelligent content-based editing), music search (e.g., indexing and recommendation of music by melody, bass, rhythm, or chord progression), and musicology (e.g., analyzing jazz improvisations and other nonnotated music). As such, AMT is an enabling technology with clear potential for both economic and societal impact. AMT is closely related to other music signal processing tasks BIB001 , such as audio source separation, which also involves the estimation and inference of source signals from mixture observations. It is also useful for many high-level tasks in music information retrieval BIB002 , such as structural segmentation, cover-song detection, and assessment of music similarity, since these tasks are much easier to address once the musical notes are known. Thus, AMT provides the main link between the fields of music signal processing and symbolic music processing (i.e., the processing of music notation and music language modeling). The integration of the two aforementioned fields through AMT will be discussed in the section "Further Extensions and Future Work." Given the potential impact of AMT, the problem has attracted commercial interest in addition to academic research. While it is outside the scope of this article to provide a comprehensive list of commercial AMT software, commonly used applications include Melodyne (http://www.celemony.com/en/melodyne), AudioScore (http://www.sibelius.com/ products/audioscore/), ScoreCloud (http:// scorecloud.com/), AnthemScore (https:// www.lunaverus.com/), and Transcribe! (https://www.seventhstring.com/xscribe/). It is worth noting that AMT papers in the literature have refrained from making explicit comparisons with commercially available music transcription software, possibly because of the difference in scope and target application between commercial and academic tools.