Music Information Retrieval Meets Music Education

This paper addresses the use of Music Information Retrieval (MIR) techniques in music education and their integration in learning software. A general overview of systems that are either commercially available or in research stage is presented. Furthermore, three well-known MIR methods used in music learning systems and their state-of-the-art are described: music transcription, solo and accompaniment track creation, and generation of performance instructions. As a representative example of a music learning system developed within the MIR community, the Songs2See software is outlined. Finally, challenges and directions for future research are described

Real-time software electric guitar audio transcription

Guitar audio transcription is the process of generating a human-interpretable musical score from guitar audio. The musical score is presented as guitar tablature, which indicates not only what notes are played, but where they are played on the guitar fretboard. Automatic transcription remains a challenge when dealing with polyphonic sounds. The guitar adds further ambiguity to the transcription problem because the same note can often be played in many ways. In this thesis work, a portable software architecture is presented for processing guitar audio in real time and providing a set of highly probable transcription solutions. Novel algorithms for performing polyphonic pitch detection and generating confidence values for transcription solutions (by which they are ranked) are also presented. Transcription solutions are generated for individual signal windows based on the output of the polyphonic pitch detection algorithm. Confidence values are generated for solutions by analyzing signal properties, fingering difficulty, and proximity to previous highest confidence solutions. The rules used for generating confidence values are based on expert knowledge of the instrument. Performance is measured in terms of algorithm accuracy, latency, and throughput. The correct result is ranked 2.08 (with the top rank being 0) for chords. The general case of various notes over time presents results that require qualitative analysis; the system in general is very susceptible to noise and has a difficult time distinguishing harmonics from actual fundamentals. By allowing the user to seed the system with a ground truth, correct recognition of future states is improved significantly in some cases. The sampling time is 250 ms with an average processing time of 110 ms, giving an average total latency of 360 ms. Throughput is 62.5 sample windows per second. Performance is not processor-bound, enabling high performance on a wide variety of personal computers

Automatic music transcription: challenges and future directions

Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects

AutoRhythmGuitar: Computer-aided Composition for Rhythm Guitar in the Tab Space

(Abstract to follow

Modeling Bends in Popular Music Guitar Tablatures

Tablature notation is widely used in popular music to transcribe and share guitar musical content. As a complement to standard score notation, tablatures transcribe performance gesture information including finger positions and a variety of guitar-specific playing techniques such as slides, hammer-on/pull-off or bends.This paper focuses on bends, which enable to progressively shift the pitch of a note, therefore circumventing physical limitations of the discrete fretted fingerboard. In this paper, we propose a set of 25 high-level features, computed for each note of the tablature, to study how bend occurrences can be predicted from their past and future short-term context. Experiments are performed on a corpus of 932 lead guitar tablatures of popular music and show that a decision tree successfully predicts bend occurrences with an F1 score of 0.71 anda limited amount of false positive predictions, demonstrating promising applications to assist the arrangement of non-guitar music into guitar tablatures

Statistical Piano Reduction Controlling Performance Difficulty

We present a statistical-modelling method for piano reduction, i.e. converting an ensemble score into piano scores, that can control performance difficulty. While previous studies have focused on describing the condition for playable piano scores, it depends on player's skill and can change continuously with the tempo. We thus computationally quantify performance difficulty as well as musical fidelity to the original score, and formulate the problem as optimization of musical fidelity under constraints on difficulty values. First, performance difficulty measures are developed by means of probabilistic generative models for piano scores and the relation to the rate of performance errors is studied. Second, to describe musical fidelity, we construct a probabilistic model integrating a prior piano-score model and a model representing how ensemble scores are likely to be edited. An iterative optimization algorithm for piano reduction is developed based on statistical inference of the model. We confirm the effect of the iterative procedure; we find that subjective difficulty and musical fidelity monotonically increase with controlled difficulty values; and we show that incorporating sequential dependence of pitches and fingering motion in the piano-score model improves the quality of reduction scores in high-difficulty cases.Comment: 12 pages, 7 figures, version accepted to APSIPA Transactions on Signal and Information Processin

Combined audio and video analysis for guitar chord identification

This thesis presents a multi-modal approach to automatically identifying guitar chords using audio and video of the performer. Chord identi cation is typically performed by analyzing the audio, using a chroma based feature to extract pitch class information, then identifying the chord with the appropriate label. Even if this method proves perfectly accurate, stringed instruments add extra ambiguity as a single chord or melody may be played in di erent positions on the fretboard. Preserving this information is important, because it signi es the original ngering, and implied \easiest" way to perform the selection. This chord identi cation system combines analysis of audio to determine the general chord scale (i.e. A major, G minor), and video of the guitarist to determine chord voicing (i.e. open, barred, inversion), to accurately identify the guitar chord.M.S., Electrical Engineering -- Drexel University, 201