Automatic Transcription of Recorded Music

Abstract

The automatic transcription of music recordings with the objective to derive as core-liker epresentation from a givenaudio representation is afundamental and challenging task. In particular for polyphonic music recordings with overlapping sound sources, current transcription systems still have problems to accurately extract the parameters of individual notes specified by pitch, onset, and duration. In this article, we present amusic transcription system that is carefully designed to cope with various facets of music. One main idea of our approach is to consistently employam id-levelr epresentation that is based on am usically meaningful pitch scale. To achieve the necessary spectral and temporal resolution, we use amulti-resolution Fourier transform enhanced by an instantaneous frequencye stimation. Subsequently,h aving extracted pitch and note onset information from this representation, we employHidden Markov Models (HMM)for determining the note events in acontext-sensitive fashion. As another contribution, we evaluate our transcription system on an extensive dataset containing audio recordings of various genre. Here, opposed to manyp revious approaches, we do not only rely on synthetic audio material, bute valuate our system on real audio recordings using MIDI-audio synchronization techniques to automatically generate reference annotations. PACS no. 43.75.Xz, 43.75.zz ACTA ACUSTICA UNITED WITH ACUSTICA Grosche et al.:A utomatic transcription of music Vol. 98 (2012) ACTA ACUSTICA UNITED WITH ACUSTICA Vol. 98 (2012)

A supervised classification approach for note tracking in polyphonic piano transcription

“…Under this premise some works have considered the use of onset information to address such issues. Examples of such works may be found in Marolt and Divjak (2002), which considered onset information for tackling the problem of tracking repeated notes, the work by Emiya, Badeau, and David (2008), in which onset information was used for segmenting the signal before the pitch estimation phase, the proposal by Iñesta and Pérez-Sancho (2013), which postprocessed the result of the MPE stage with the aim of correcting timing issues with onset information, or the system by Grosche et al (2012), which also considered onset information under an HMM framework. Note that, while scarce, some works as the one by Benetos and Dixon (2011) have considered both onset and offset estimation systems for tackling these timing issues.…”

“…Automatic Music Transcription (AMT) stands for the process of automatically retrieving a high-level symbolic representation of the music content present in an audio signal (Grosche et al, 2012). This particular task has been largely studied and addressed by the Music Information Retrieval (MIR) field due to its considerable application in a number of tasks such as music preservation and annotation (Kroher et al, 2016), music similarity and retrieval (Lidy et al, 2010), and computational musicological analysis (Klapuri & Davy, 2007), among others.…”

Automatic music transcription: challenges and future directions

“…Another approach for feature-based AMT was proposed in [113], which uses genetic algorithms for estimating a transcription by mutating the solution until it matches a similarity criterion between the original signal and the synthesized transcribed signal. More recently, Grosche et al [61] proposed an AMT method based on a mid-level representation derived from a multiresolution Fourier transform combined with an instantaneous frequency estimation. The system also combines onset detection and tuning estimation for computing framebased estimates.…”

“…Information from an onset detection function was also incorporated. In addition, context-dependent HMMs were employed in [61] for determining note events by combining the output of a multi-pitch detection system with an onset detection system. Finally, dynamic Bayesian networks (DBNs) were proposed in [109] for note tracking using as input the pitch activation of an NMF-based multi-pitch detection algorithm.…”

“…Also, the field of content-based music information retrieval, which refers to automated processing of music for search and retrieval purposes and includes the AMT problem, is discussed in [22]. A recent state-of-the-art review of music signal analysis (which includes AMT) is given in [92] while the work by Grosche et al [61] includes a recent state-of-the-art section on AMT systems.…”