From MIDI to traditional musical notation

This paper addresses the problem of the real-time automatic transcription of a live music performance into a symbolic format. The source data are given by any music instrument or other device able to communicate through a performance protocol. During a performance, music events are parsed and their parameters are evaluated thanks to rhythm and pitch detection algorithms. The final step is the creation of a well-formed XML document, validated against the new international standard known as IEEE 1599. This work will shortly describe both the software environment and the XML format, but the main analysis will involve the real-time recognition of music events. Finally, a case study will be presented: PureMX, a set of Pure Data externals, able to perform the automatic transcription of MIDI events.

Multimedia, Seventh IEEE …, 2005

In this study, we propose a new approach to extract monophonic melody from MIDI files and provide a comparison of existing methods. Our approach is based on the elimination of MIDI channels those do not contain melodic information. First, MIDI channels are clustered depending on pitch histogram. Afterwards, a channel is selected from each cluster as representative and remaining channels and their notes are removed. Finally, Skyline algorithm is applied on the modified MIDI set to ensure accuracy of monophonic melody. We evaluated our approach within a test bed of MIDI files, composed of variable music styles. Both our approach and the results from experiments are presented in detail.

2004

Although modern audio score following systems work very well with low polyphony performances, they are still too imprecise with highly polyphonic instruments such as the piano, or the guitar. On the other hand, these instruments can easily output Midi information which shows that our work on robust Midi score following is still needed. We propose an adaptation to Midi input of our HMM-based stochastic audio score follower, focusing the attention on the piano as our test instrument. The acoustic salience of the Midi notes is modeled by an amplitude envelope, taking into account the sustain pedal, from which note match and attack probabilities are derived. Tests with a complex piano piece played with many errors showed a very high robustness.

2002

This paper describes a Hidden Markov Model (HMM)-based method of automatic transcription of MIDI (Musical Instrument Digital Interface) signals of performed music. The problem is formulated as recognition of a given sequence of fluctuating note durations to find the most likely intended note sequence utilizing the modern continuous speech recognition technique. Combining a stochastic model of deviating note durations and a stochastic grammar representing possible sequences of notes, the maximum likelihood estimate of the note sequence is searched in terms of Viterbi algorithm. The same principle is successfully applied to a joint problem of bar line allocation, time measure recognition, and tempo estimation. Finally, durations of consecutive ¤ notes are combined to form a "rhythm vector" representing tempo-free relative durations of the notes and treated in the same framework. Significant improvements compared with conventional "quantization" techniques are shown.

Tools and Methodologies, 2008

State-of-the-art MIR issues are presented and discussed both from the symbolic and audio points of view. As for the symbolic aspects, different approaches are presented in order to provide an overview of the different available solutions for particular MIR tasks. This section ends with an overview of MX, the IEEE standard XML language specifically designed to support interchange between musical notation, performance, analysis, and retrieval applications. As for the audio level, first we focus on blind tasks like beat and tempo tracking, pitch tracking and automatic recognition of musical instruments. Then we present algorithms that work both on compressed and uncompressed data. We analyze the relationships between MIR and feature extraction presenting examples of possible applications. Finally we focus on automatic music synchronization and we introduce a new audio player that supports the MX logic layer and allows to play both score and audio coherently.

1998

The aim of this tutorial paper is to introduce and discuss different approaches to the automatic music transcription problem. The task is here understood as a transformation from an acoustic sig- nal into a MIDI-like symbolic representation. Algorithms are dis- cussed that concern three subproblems. (i) Estimation of the temporal structure of acoustic musical signals, the musical meter. (ii) Estimation

This paper presents a system that translates the captured or recorded melody into musical notations automatically and instantly into a developed stave whereby a musician can compose music directly without any extra process or procedure, in real-time environments. In this translating system, the frequency of a captured or recorded melody is first analyzed through a microphone or musical instrument for its fundamental frequency. The analyzed fundamental frequency is then compared with the predefined frequency of musical notes. The matched musical note frequency will be distinguished at the developed musical stave interface instantly. This developed system can facilitate a composer in automatically translate his melody to musical notes without having to manually writing it down based on the melody he plays.

2006

As machines become more and more portable, and part of our everyday life, it becomes apparent that developing interactive and ubiquitous systems is an important aspect of new music applications created by the research community. We are interested in developing a robust layer for the automatic annotation of audio signals, to be used in various applications, from music search engines to interactive installations, and in various contexts, from embedded devices to audio content servers. We propose adaptations of existing signal processing techniques to a real time context. Amongst these annotation techniques, we concentrate on low and mid-level tasks such as onset detection, pitch tracking, tempo extraction and note modelling. We present a framework to extract these annotations and evaluate the performances of different algorithms. The first task is to detect onsets and offsets in audio streams within short latencies. The segmentation of audio streams into temporal objects enables vario...

Journal of emerging technologies and innovative research, 2019

In this paper, we are proposing the idea of making an automated software that will transcribe each note while the musician plays the instrument. The software will take the sound of the instrument as an input and will process the frequency of each note and the way it is played and transcribe the note visually. In this project, we will consider recording consisting of only monophonic notes i.e. only a single note will be played at a time.This paper focuses on extracting audio, detecting pitch and displaying symbols. The project makes extent use of audio signal processing and python libraries (pyAudio).. IndexTerms audio signal processing, pyAudio.

Proceedings of the SMC Conferences, 2019

Melody identification is an important early step in music analysis. This paper presents a tool to identify the melody in each measure of a Standard MIDI File. We also share an open dataset of manually labeled music for researchers. We use a Bayesian maximum-likelihood approach and dynamic programming as the basis of our work. We have trained parameters on data sampled from the million song dataset [1, 2] and tested on a dataset including 1703 measures of music from different genres. Our algorithm achieves an overall accuracy of 89% in the test dataset. We compare our results to previous work.

Proceedings of the 9th ACM SIGPLAN International Workshop on Functional Art, Music, Modelling, and Design, 2021

This article discusses the internal architecture of the MidifilePerformer application. This software allows a user to follow a score described in the MIDI format at its own pace and with its own accentuation. MidifilePerformer allows for a wide variety of style and interpretation to be applied to the vast number of MIDI files found on the Internet. We present here the algorithms enabling the association between the commands made by the performer, via a MIDI or alphanumeric keyboard, and the notes appearing in the score. We will show that these algorithms define a notion of expressiveness which extends the possibilities of interpretation while maintaining the simplicity of the gesture.

IEEE Multimedia, 2009

This paper presents a framework for authoring, storing, retrieving, and presenting music lectures on the Web. For a synchronized presentation between score and recorded performance audio, we propose a dynamic programming-based algorithm for MIDI-to-Wave alignment to explore the temporal relations between MIDI and the corresponding performance recording. With rapid advances in music transcription technology, it had become more possible to align MIDI and wave in a symbolic domain. However, transcription errors usually occur when transcribing polyphonic music or multi-instruments music because the complex harmonic of different instruments. The proposed alignment algorithm works in the symbolic domain even if many transcription errors have occurred. The aligned MIDI and wave can be attached to many kinds of teaching materials. With a synchronized presentation, learners can read music scores and get instructional information when listening to certain sections of music pieces. We built an evaluation system for doing a subjective evaluation. The percentage of bars which were regarded as aligned perfectly and aligned within acceptable limits is 95.63%. The questionnaire in the evaluation system also reported positive opinions from both engineers and musicians.

2006

Standard MIDI files contain data that can be considered as a symbolic representation of music (a digital score), and most of them are structured as a number of tracks. One of them usually contains the melodic line of the piece, while the other tracks contain accompaniment music. The goal of this work is to identify the track that contains the melody using statistical properties of the musical content and pattern recognition techniques. Finding that track is very useful for a number of applications, like speeding up melody matching when searching in MIDI databases or motif extraction, among others. First, a set of descriptors from each track of the target file are extracted. These descriptors are the input to a random forest classifier that assigns the probability of being a melodic line to each track. The track with the highest probability is selected as the one containing the melodic line of that MIDI file. Promising results have been obtained testing a number of databases of different music styles.

Journal of New Music Research, 2008

Despite the importance of the note as the basic representational symbol in Western music notation, the explicit and accurate recognition of musical notes has been a difficult problem in automatic music transcription research. In fact, most approaches disregard the importance of notes as musicological units having dynamic nature.In this paper we propose a mechanism for quantizing the temporal sequences of the detected fundamental frequencies into note symbols, characterized by precise temporal boundaries and note pitches (namely, MIDI note numbers). The developed method aims to cope with typical dynamics and performing styles such as vibrato, glissando or legato.