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

The role of the Protein Kinase CKII in the life cycle of Human Papillomaviruses

Cervical cancer is the fourth most common cancer in women and virtually all cases can be traced back to a preceding infection with a high-risk HPV (human papillomavirus) type. Worldwide 10% of all women are infected, with the hotspots being South America and Africa. The best way to fight cervical cancer is to prohibit its formation. Currently, only vaccines are available to achieve this, but these are not the ultimate solution, as they are not able to clear an existing HPV infection. If we want to reduce the cancer risk of the millions of women infected with HPV today, we need a supplement, which is able to clear a persisting HPV infection reliably. In this thesis it is shown that the catalytic subunit of the protein kinase CK2 plays an important role in the initial amplification phase of HPV and an interference leads to lesser replication rates of viral genomes. Additionally, a CK2 inhibitor (CX-4945) is tested for its ability to inhibit CK2 and through this to hinder HPV replication. It is shown that this compound could be the basis for the long sought anti-HPV drug

Evolution of Music by Public Choice

Music evolves as composers, performers, and consumers favor some musical variants over others. To investigate the role of consumer selection, we constructed a Darwinian music engine consisting of a population of short audio loops that sexually reproduce and mutate. This population evolved for 2,513 generations under the selective influence of 6,931 consumers who rated the loops’ aesthetic qualities. We found that the loops quickly evolved into music attributable, in part, to the evolution of aesthetically pleasing chords and rhythms. Later, however, evolution slowed. Applying the Price equation, a general description of evolutionary processes, we found that this stasis was mostly attributable to a decrease in the fidelity of transmission. Our experiment shows how cultural dynamics can be explained in terms of competing evolutionary forces

Intonation in unaccompanied singing: Accuracy, drift, and a model of reference pitch memory

Copyright 2014 Acoustical Society of America. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the Acoustical Society of America. The following article appeared in J. Acoust. Soc. Am. 136, 401 (2014) and may be found at http://dx.doi.org/10.1121/1.4881915

Морфологічні зміни підщелепної слинної залози при гіпертонічній хворобі

Вступ. Значна увага приділяється питанням, пов'язаним з дисциркуляторними змінами в різних органах, пов’язаних з артеріальною гіпертензією і, зокрема, у великих слинних залозах. Доступна література містить недостатньо інформації про морфологічні зміни в підщелепних слинних залозах людини при гіпертонічній хворобі. Метою роботи є вивчення морфофункціональних змін внутрішньоорганних судин і тканин підщелепної слинної залози у хворих на гіпертонічну хворобу

The deep history of music project

Music, like language and genes, is the product of a descent-by-modification process. As such, the current distribution of music styles around the world should reflect the history of human migration and cultural diffusion (Lomax, 1968). However, where geneticists and linguists have developed sophisticated techniques for reconstructing that history, ethnomusicologist have largely abandoned large-scale comparative studies. here we outline a proposal to revive comparative musicology using recent digitized ethnomusicological archives and MIR technology. Our study has three objectives: (i) To determine global distribution of musical style; (ii) to investigate the relationship between patterns of musical, linguistic and genetic diversity; (iii) to construct a large open-access database containing MIR features and metadata from traditional music

Improving music genre classification using automatically induced harmony rules

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates

Magnifying transmitter

The magnifying transmitter was intended by Nicola Tesla for the wireless transmission of electrical energy. It is a high power harmonic oscillator, an air-core, multiple-resonant transformer that can generate very high voltages. In normal operation the magnifying transmitter is relatively silent, generating a high power electric field, but if the output voltage exceeds the design voltage of the elevated terminal, high-voltage sparks will strike out from the electrode into the air. In his autobiography, Tesla stated that "...I feel certain that of all my inventions, the Magnifying Transmitter will prove most important and valuable to future generations.