4,597 research outputs found
Performance Following: Real-Time Prediction of Musical Sequences Without a Score
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Drum Transcription via Classification of Bar-level Rhythmic Patterns
acceptedMatthias Mauch is supported by a Royal Academy of Engineering
Research Fellowshi
Real-Time Audio-to-Score Alignment of Music Performances Containing Errors and Arbitrary Repeats and Skips
This paper discusses real-time alignment of audio signals of music
performance to the corresponding score (a.k.a. score following) which can
handle tempo changes, errors and arbitrary repeats and/or skips (repeats/skips)
in performances. This type of score following is particularly useful in
automatic accompaniment for practices and rehearsals, where errors and
repeats/skips are often made. Simple extensions of the algorithms previously
proposed in the literature are not applicable in these situations for scores of
practical length due to the problem of large computational complexity. To cope
with this problem, we present two hidden Markov models of monophonic
performance with errors and arbitrary repeats/skips, and derive efficient
score-following algorithms with an assumption that the prior probability
distributions of score positions before and after repeats/skips are independent
from each other. We confirmed real-time operation of the algorithms with music
scores of practical length (around 10000 notes) on a modern laptop and their
tracking ability to the input performance within 0.7 s on average after
repeats/skips in clarinet performance data. Further improvements and extension
for polyphonic signals are also discussed.Comment: 12 pages, 8 figures, version accepted in IEEE/ACM Transactions on
Audio, Speech, and Language Processin
PERFORMANCE FOLLOWING: TRACKING A PERFORMANCE WITHOUT A SCORE
EPSRC Doctoral Training Award; EPSRC Leadership Fellowshi
Automatic accompaniment of vocal melodies in the context of popular music
A piece of popular music is usually defined as a combination of vocal melody and instrumental accompaniment. People often start with the melody part when they are trying to compose or reproduce a piece of popular music. However, creating appropriate instrumental accompaniment part for a melody line can be a difficult task for non-musicians. Automation of accompaniment generation for vocal melodies thus can be very useful for those who are interested in singing for fun. Therefore, a computer software system which is capable of generating harmonic accompaniment for a given vocal melody input has been presented in this thesis. This automatic accompaniment system uses a Hidden Markov Model to assign chord to a given part of melody based on the knowledge learnt from a bank of vocal tracks of popular music. Comparing with other similar systems, our system features a high resolution key estimation algorithm which is helpful to adjust the generated accompaniment to the input vocal. Moreover, we designed a structure analysis subsystem to extract the repetition and structure boundaries from the melody. These boundaries are passed to the chord assignment and style player subsystems in order to generate more dynamic and organized accompaniment. Finally, prototype applications are discussed and the entire system is evaluated.M.S.Committee Chair: Chordia, Parag; Committee Member: Freeman, Jason; Committee Member: Weinberg, Gi
Interactive real-time musical systems
PhDThis thesis focuses on the development of automatic accompaniment systems.
We investigate previous systems and look at a range of approaches
that have been attempted for the problem of beat tracking. Most beat
trackers are intended for the purposes of music information retrieval where
a `black box' approach is tested on a wide variety of music genres. We
highlight some of the diffculties facing offline beat trackers and design a
new approach for the problem of real-time drum tracking, developing a
system, B-Keeper, which makes reasonable assumptions on the nature of
the signal and is provided with useful prior knowledge.
Having developed the system with offline studio recordings, we look to
test the system with human players. Existing offline evaluation methods
seem less suitable for a performance system, since we also wish to evaluate
the interaction between musician and machine. Although statistical data
may reveal quantifiable measurements of the system's predictions and behaviour,
we also want to test how well it functions within the context of a
live performance. To do so, we devise an evaluation strategy to contrast
a machine-controlled accompaniment with one controlled by a human.
We also present recent work on a real-time multiple pitch tracking,
which is then extended to provide automatic accompaniment for harmonic
instruments such as guitar. By aligning salient notes in the output from
a dual pitch tracking process, we make changes to the tempo of the
accompaniment in order to align it with a live stream. By demonstrating
the system's ability to align offline tracks, we can show that under
restricted initial conditions, the algorithm works well as an alignment tool
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