2,967 research outputs found
Music Information Retrieval for Irish Traditional Music Automatic Analysis of Harmonic, Rhythmic, and Melodic Features for EfïŹcient Key-Invariant Tune Recognition
Music making and listening practices increasingly rely on techno logy,and,asaconsequence,techniquesdevelopedinmusicinformation retrieval (MIR) research are more readily available to end users, in par ticular via online tools and smartphone apps. However, the majority of MIRresearchfocusesonWesternpopandclassicalmusic,andthusdoes not address speciïŹcities of other musical idioms. Irishtraditionalmusic(ITM)ispopularacrosstheglobe,withregular sessionsorganisedonallcontinents. ITMisadistinctivemusicalidiom, particularly in terms of heterophony and modality, and these character istics can constitute challenges for existing MIR algorithms. The bene ïŹtsofdevelopingMIRmethodsspeciïŹcallytailoredtoITMisevidenced by Tunepal, a query-by-playing tool that has become popular among ITM practitioners since its release in 2009. As of today, Tunepal is the state of the art for tune recognition in ITM. The research in this thesis addresses existing limitations of Tunepal. The main goal is to ïŹnd solutions to add key-invariance to the tune re cognitionsystem,animportantfeaturethatiscurrentlymissinginTune pal. Techniques from digital signal processing and machine learning are used and adapted to the speciïŹcities of ITM to extract harmonic iv and temporal features, respectively with improvements on existing key detection methods, and a novel method for rhythm classiïŹcation. These featuresarethenusedtodevelopakey-invarianttunerecognitionsystem that is computationally efïŹcient while maintaining retrieval accuracy to a comparable level to that of the existing system
A novel chroma representation of polyphonic music based on multiple pitch tracking techniques
It is common practice to map the frequency content of music onto a chroma representation, but there exist many different schemes for constructing such a representation. In this paper, a new scheme is proposed. It comprises a detection of salient frequencies, a conversion of salient frequencies to notes, a psychophysically motivated weighting of harmonics in support of a note, a restriction of harmonic relations between different notes and a restriction of the deviations from a predefined pitch scale (e.g. the equally tempered western scale). A large-scale experimental evaluation has confirmed that the novel chroma representation more closely matches manual chord labels than the representations generated by six other tested schemes. Therefore, the new chroma representation is expected to improve applications such as song similarity matching and chord detection and labeling
Automatic chord transcription from audio using computational models of musical context
PhDThis thesis is concerned with the automatic transcription of chords from audio, with an emphasis
on modern popular music. Musical context such as the key and the structural segmentation aid
the interpretation of chords in human beings. In this thesis we propose computational models
that integrate such musical context into the automatic chord estimation process.
We present a novel dynamic Bayesian network (DBN) which integrates models of metric
position, key, chord, bass note and two beat-synchronous audio features (bass and treble
chroma) into a single high-level musical context model. We simultaneously infer the most probable
sequence of metric positions, keys, chords and bass notes via Viterbi inference. Several
experiments with real world data show that adding context parameters results in a significant
increase in chord recognition accuracy and faithfulness of chord segmentation. The proposed,
most complex method transcribes chords with a state-of-the-art accuracy of 73% on the song
collection used for the 2009 MIREX Chord Detection tasks. This method is used as a baseline
method for two further enhancements.
Firstly, we aim to improve chord confusion behaviour by modifying the audio front end
processing. We compare the effect of learning chord profiles as Gaussian mixtures to the effect
of using chromagrams generated from an approximate pitch transcription method. We show
that using chromagrams from approximate transcription results in the most substantial increase
in accuracy. The best method achieves 79% accuracy and significantly outperforms the state of
the art.
Secondly, we propose a method by which chromagram information is shared between
repeated structural segments (such as verses) in a song. This can be done fully automatically
using a novel structural segmentation algorithm tailored to this task. We show that the technique
leads to a significant increase in accuracy and readability. The segmentation algorithm itself
also obtains state-of-the-art results. A method that combines both of the above enhancements
reaches an accuracy of 81%, a statistically significant improvement over the best result (74%)
in the 2009 MIREX Chord Detection tasks.Engineering and Physical Research Council U
PowerSpy: Location Tracking using Mobile Device Power Analysis
Modern mobile platforms like Android enable applications to read aggregate
power usage on the phone. This information is considered harmless and reading
it requires no user permission or notification. We show that by simply reading
the phone's aggregate power consumption over a period of a few minutes an
application can learn information about the user's location. Aggregate phone
power consumption data is extremely noisy due to the multitude of components
and applications that simultaneously consume power. Nevertheless, by using
machine learning algorithms we are able to successfully infer the phone's
location. We discuss several ways in which this privacy leak can be remedied.Comment: Usenix Security 201
Form as meter: metric forms through Fourier space
The Discrete Fourier Transform, which was initially mentioned in the music theory domain by David Lewin, is an analytical tool developed by Ian Quinn, and later expanded by theorists such as Jason Yust, William Sethares, and Andrew Milne. Though it was originally designed for pitch-class spaces, Emmanuel Amiot has explored the DFTâs implementation into the rhythmic domain, and has recently used it to unravel mathematical problems in music. An explanation of the DFT model will be made available here to a reader requiring only fundamental arithmetic. Throughout this thesis, I intend to explore the DFT in the music of various composers to demonstrate applicability, and will argue for a metric conception of form
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