Rachel M. Bittner

Kernel Additive Modeling for interference reduction in multi-channel music recordings

When recording a live musical performance, the different voices, such as the instrument groups or soloists of an orchestra, are typically recorded in the same room simultaneously, with at least one microphone assigned to each voice. However, it is difficult to acoustically shield the microphones. In practice, each one contains interference from every other voice. In this paper, we aim to reduce these interferences in multi-channel recordings to recover only the isolated voices. Following the recently proposed Kernel Additive Modeling framework, we present a method that iteratively estimates both the power spectral density of each voice and the corresponding strength in each microphone signal. With this information, we build an optimal Wiener filter, strongly reducing interferences. The trade-off between distortion and separation can be controlled by the user through the number of iterations of the algorithm. Furthermore, we present a computationally effective approximation of the iterative procedure. Listening tests demonstrate the effectiveness of the method.

Towards the characterization of singing styles in world music

In this paper we focus on the characterization of singing styles in world music. We develop a set of contour features capturing pitch structure and melodic embellishments. Using these features we train a binary classifier to distinguish vocal from non-vocal contours and learn a dictionary of singing style elements. Each contour is mapped to the dictionary elements and each recording is summarized as the histogram of its contour mappings. We use K-means clustering on the recording representations as a proxy for singing style similarity. We observe clusters distinguished by characteristic uses of singing techniques such as vibrato and melisma. Recordings that are clustered together are often from neighbouring countries or exhibit aspects of language and cultural proximity. Studying singing particularities in this comparative manner can contribute to understanding the interaction and exchange between world music styles.

Signal processing methods for removing the effects of whole-body vibration upon speech

Humans may be exposed to whole-body vibration in environments where clear speech communications are crucial, particularly during the launch phase of space flight and in high-performance aircraft. Prior research has shown that high levels of vibration cause a decrease in speech intelligibility. However, the effects of whole-body vibration upon speech are not well understood, and no attempt has been made to restore speech distorted by whole-body vibration. In this paper, a model for speech during whole-body vibration is proposed and a method to remove its effect is described. The method presented reduces the perceptual effects of vibration, yields higher automatic speech recognition accuracy scores, and may significantly improve intelligibility. Possible applications include incorporation within spaceflight, aviation, or off-road vehicle radio-communication systems.

Pitch contour tracking in music using Harmonic Locked Loops

We present a novel time-domain pitch contour tracking algorithm based on Harmonic Locked Loops, which differs from existing method in terms of its approach, resolution, timbre information and speed. In addition to estimating pitch contours, the proposed method computes the amplitude of each harmonic over time, expanding the potential set of features that can be used for higher level tasks such as melody extraction. The method is tested against ground truth melody pitch annotations from publicly available datasets, and we show that contour recall is improved compared with a state of the art approach.