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Author

Martin Weiss Hansen

Other affiliations: MediaTech Institute
Bio: Martin Weiss Hansen is an academic researcher from Aalborg University. The author has contributed to research in topics: Panning (audio) & Estimator. The author has an hindex of 3, co-authored 9 publications receiving 25 citations. Previous affiliations of Martin Weiss Hansen include MediaTech Institute.

Papers
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Journal ArticleDOI
TL;DR: The proposed method is based on a signal model that includes the panning parameters of the sources in a stereophonic mixture, such as those applied artificially in a recording studio, and provides a full parameterization of the components of the observed signal.
Abstract: In this paper, a method for multi-pitch estimation of stereophonic mixtures of harmonic signals, e.g., instrument recordings, is presented. The proposed method is based on a signal model that includes the panning parameters of the sources in a stereophonic mixture, such as those applied artificially in a recording studio. If the sources in a mixture have different panning parameters, this diversity can be used to simplify the pitch estimation problem. The mixing parameters of the sources might be shared, resulting in a multi-pitch estimation problem, which is solved using an approach based on an expectation–maximization algorithm for Gaussian sources, where the fundamental frequencies and model orders are estimated jointly. The fundamental frequencies may be related, resulting in overlapping harmonics, complicating the estimation of the parameters. A codebook of harmonic amplitude vectors is trained on recordings of instruments playing single notes, and used when estimating the amplitudes of the mixture components. The proposed method is evaluated using stereophonic mixtures of instrument recordings and is compared to state-of-the-art transcription and multi-pitch estimation methods. Experiments show an increase in performance when knowledge about the panning parameters is taken into account. The proposed method provides a full parameterization of the components of the observed signal. Possible applications include instrument tuning, audio editing tools, modification of harmonic mixture components, and audio effects.

7 citations

Proceedings ArticleDOI
28 Dec 2015
TL;DR: The proposed method is based on a signal model that takes into account a stereophonic mixture created by mixing multiple individual channels with different pan parameters, and is hence suited for use in automatic music transcription, source separation and classification systems.
Abstract: In this paper, a novel method for pitch estimation of stereophonic mixtures is presented, and it is investigated how the performance is affected by the pan parameters of the individual signals of the mixture. The method is based on a signal model that takes into account a stereophonic mixture created by mixing multiple individual channels with different pan parameters, and is hence suited for use in automatic music transcription, source separation and classification systems. Panning is done using both amplitude differences and delays. The performance of the estimator is compared to one single-channel, two multi-channel and one multi-pitch estimator using synthetic and real signals. Experiments show that the proposed method is able to correctly estimate the pitches of a mixture of three real signals when they are separated by more than 25 degrees.

6 citations

Proceedings ArticleDOI
01 Mar 2017
TL;DR: Experiments show an increase in performance when knowledge about the panning parameters is utilized together with the codebook of magnitude amplitudes when compared to a state-of-the-art transcription method.
Abstract: In this paper, a method for multi-pitch estimation of stereophonic mixtures of multiple harmonic signals is presented. The method is based on a signal model which takes the amplitude and delay panning parameters of the sources in a stereophonic mixture into account. Furthermore, the method is based on the extended invariance principle (EXIP), and a codebook of realistic amplitude vectors. For each fundamental frequency candidate in each of the sources, the amplitude estimates are mapped to entries in the codebook, and the pitch and model order are estimated jointly. The performance of the proposed method is evaluated using mixtures of real signals. Experiments show an increase in performance when knowledge about the panning parameters is utilized together with the codebook of magnitude amplitudes when compared to a state-of-the-art transcription method.

4 citations

04 Sep 2018
TL;DR: The proposed method for separating stereophonic mixtures into their harmonic constituents is based on a harmonic signal model and can be used for source re-panning, remixing, and multi-channel upmixing, e.g. for hi-fi systems with multiple loudspeakers.
Abstract: In this paper, a method for separating stereophonic mixtures into their harmonic constituents is proposed. The method is based on a harmonic signal model. An observed mixture is decomposed by first estimating the panning parameters of the sources, and then estimating the fundamental frequencies and the amplitudes of the harmonic components. The number of sources and their panning parameters are estimated using an approach based on clustering of narrowband interaural level and time differences. The panning parameter distribution is modelled as a Gaussian mixture and the generalized variance is used for selecting the number of sources. The fundamental frequencies of the sources are estimated using an iterative approach. To enforce spectral smoothness when estimating the fundamental frequencies, a codebook of magnitude amplitudes is used to limit the amount of energy assigned to each harmonic. The source models are used to form Wiener filters which are used to reconstruct the sources. The proposed method can be used for source re-panning (demonstration given), remixing, and multi-channel upmixing, e.g. for hi-fi systems with multiple loudspeakers.

3 citations

Proceedings Article
01 Jan 2014
TL;DR: It is concluded that there is no single measure of photorealism that is appropriate in all situations, and photrealism appears to be a multifaceted phenomenon that requires different measurement procedures for different use scenarios.
Abstract: While the concept of photorealism has important applications in computer graphics, the research community has not agreed on a definition of photorealism that specifies how to measure it. We employed two different test procedures, which correspond to different use scenarios, in order to determine the photorealism of a virtual reconstruction of a historic Viking building using two different lighting techniques. Even in this limited case, the measured degree of photorealism appears to depend on both the test procedure as well as the tested imagery; therefore, we conclude that there is no single measure of photorealism that is appropriate in all situations. Instead, photorealism appears to be a multifaceted phenomenon that requires different measurement procedures for different use scenarios. CR Categories: I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Color, shading, shadowing, and texture; H.1.2 [Models and Principles]: User/Machine Systems—Human factors

2 citations


Cited by
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Proceedings ArticleDOI
12 May 2019
TL;DR: The plucking position estimator is the minimizer of the log spectral distance between the amplitudes of the observed signal and the plucking model and it is evaluated in proof-of-concept experiments with sudden changes of string, fret and plucking positions, which can be estimated accurately.
Abstract: In this paper a fast yet effective method is proposed for analyzing guitar performances. Specifically, the activated string and fret as well as the location of the plucking event along the guitar string are extracted from guitar signal recordings. The method is based on a parametric pitch estimator and is derived from a physically meaningful model that includes inharmonicity. A maximum a posteriori classifier is proposed, which requires training data captured from only one fret per string. The classifier is tested on recordings of electric and acoustic guitar and performs well: the average absolute error of string and fret classification is 1.5%, while the error rate varies depending on the fret used for training. The plucking position estimator is the minimizer of the log spectral distance between the amplitudes of the observed signal and the plucking model and it is evaluated in proof-of-concept experiments with sudden changes of string, fret and plucking positions, which can be estimated accurately. Unlike the state of the art, the proposed method works on very short segments, which makes it suitable for high-tempo and real-time applications.

9 citations

Journal ArticleDOI
TL;DR: The proposed method is based on a signal model that includes the panning parameters of the sources in a stereophonic mixture, such as those applied artificially in a recording studio, and provides a full parameterization of the components of the observed signal.
Abstract: In this paper, a method for multi-pitch estimation of stereophonic mixtures of harmonic signals, e.g., instrument recordings, is presented. The proposed method is based on a signal model that includes the panning parameters of the sources in a stereophonic mixture, such as those applied artificially in a recording studio. If the sources in a mixture have different panning parameters, this diversity can be used to simplify the pitch estimation problem. The mixing parameters of the sources might be shared, resulting in a multi-pitch estimation problem, which is solved using an approach based on an expectation–maximization algorithm for Gaussian sources, where the fundamental frequencies and model orders are estimated jointly. The fundamental frequencies may be related, resulting in overlapping harmonics, complicating the estimation of the parameters. A codebook of harmonic amplitude vectors is trained on recordings of instruments playing single notes, and used when estimating the amplitudes of the mixture components. The proposed method is evaluated using stereophonic mixtures of instrument recordings and is compared to state-of-the-art transcription and multi-pitch estimation methods. Experiments show an increase in performance when knowledge about the panning parameters is taken into account. The proposed method provides a full parameterization of the components of the observed signal. Possible applications include instrument tuning, audio editing tools, modification of harmonic mixture components, and audio effects.

7 citations

Journal ArticleDOI
TL;DR: The proposed pseudo 2-D spectrum-based pitch estimation method exploits the harmonic structure of pitched sounds in a two-dimensional frequency plane, and can work in the case where some notes contain few harmonics, and the harmonic overlap proportions are reduced greatly in the harmony cases.
Abstract: Multi-pitch estimation is a fundamental and key problem in music information retrieval, but still remains challenging due to the intrinsic complexity of polyphonic music. To address this problem, a pseudo 2-D spectrum-based method is proposed in this article. The pseudo 2-D spectrum is first constructed to map the time domain signal into the 2-D frequency space, where the harmonic signal exhibits a typical 2-D pattern. Then, pitch estimation is carried out by cross-correlation between the pseudo 2-D spectrum and the fixed 2-D harmonic template. Finally, the pitches of adjacent frames are grouped into pitch contours, where the contours whose lengths are shorter than the minimum note length limitation are discarded. And the remained pitches are refined using the estimates of neighboring frames by removing probable errors and reconstructing estimates. The proposed method exploits the harmonic structure of pitched sounds in a two-dimensional frequency plane, can work in the case where some notes contain few harmonics, and the harmonic overlap proportions are reduced greatly in the harmony cases. The experimental results show that the proposed method achieves promising performance comparing with the state-of-the-art methods on the evaluation datasets, and outperforms the bispectrum-based method on both evaluation datasets.

7 citations