951 research outputs found

    2D to 3D ambience upmixing based on perceptual band allocation

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    3D multichannel audio systems employ additional elevated loudspeakers in order to provide listeners with a vertical dimension to their auditory experience. Listening tests were conducted to evaluate the feasibility of a novel vertical upmixing technique called “perceptual band allocation (PBA),” which is based on a psychoacoustic principle of vertical sound localization, the “pitch height” effect. The practical feasibility of the method was investigated using 4-channel ambience signals recorded in a reverberant concert hall using the Hamasaki-Square microphone technique. Results showed that the PBA-upmixed 3D stimuli were significantly stronger than or similar to 9-channel 3D stimuli in 3D listener-envelopment (LEV), depending on the sound source and the crossover frequency of PBA. They also significantly produced greater 3D LEV than the 7-channel 3D stimuli. For the preference tests, the PBA stimuli were significantly preferred over the original 9-channel stimuli

    Scattering by two spheres: Theory and experiment

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    Locating and extracting acoustic and neural signals

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    This dissertation presents innovate methodologies for locating, extracting, and separating multiple incoherent sound sources in three-dimensional (3D) space; and applications of the time reversal (TR) algorithm to pinpoint the hyper active neural activities inside the brain auditory structure that are correlated to the tinnitus pathology. Specifically, an acoustic modeling based method is developed for locating arbitrary and incoherent sound sources in 3D space in real time by using a minimal number of microphones, and the Point Source Separation (PSS) method is developed for extracting target signals from directly measured mixed signals. Combining these two approaches leads to a novel technology known as Blind Sources Localization and Separation (BSLS) that enables one to locate multiple incoherent sound signals in 3D space and separate original individual sources simultaneously, based on the directly measured mixed signals. These technologies have been validated through numerical simulations and experiments conducted in various non-ideal environments where there are non-negligible, unspecified sound reflections and reverberation as well as interferences from random background noise. Another innovation presented in this dissertation is concerned with applications of the TR algorithm to pinpoint the exact locations of hyper-active neurons in the brain auditory structure that are directly correlated to the tinnitus perception. Benchmark tests conducted on normal rats have confirmed the localization results provided by the TR algorithm. Results demonstrate that the spatial resolution of this source localization can be as high as the micrometer level. This high precision localization may lead to a paradigm shift in tinnitus diagnosis, which may in turn produce a more cost-effective treatment for tinnitus than any of the existing ones

    Real-time Sound Source Separation For Music Applications

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    Sound source separation refers to the task of extracting individual sound sources from some number of mixtures of those sound sources. In this thesis, a novel sound source separation algorithm for musical applications is presented. It leverages the fact that the vast majority of commercially recorded music since the 1950s has been mixed down for two channel reproduction, more commonly known as stereo. The algorithm presented in Chapter 3 in this thesis requires no prior knowledge or learning and performs the task of separation based purely on azimuth discrimination within the stereo field. The algorithm exploits the use of the pan pot as a means to achieve image localisation within stereophonic recordings. As such, only an interaural intensity difference exists between left and right channels for a single source. We use gain scaling and phase cancellation techniques to expose frequency dependent nulls across the azimuth domain, from which source separation and resynthesis is carried out. The algorithm is demonstrated to be state of the art in the field of sound source separation but also to be a useful pre-process to other tasks such as music segmentation and surround sound upmixing
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