Multi-layered Cepstrum for Instantaneous Frequency Estimation

We propose the multi-layered cepstrum (MLC) method to estimate multiple fundamental frequencies (MF0) of a signal under challenging contamination such as high-pass filter noise. Taking the operation of cepstrum (i.e., Fourier transform, filtering, and nonlinear activation) recursively, MLC is shown as an efficient method to enhance MF0 saliency in a step-by-step manner. Evaluation on a real-world polyphonic music dataset under both normal and low-fidelity conditions demonstrates the potential of MLC.Comment: In 2018 6th IEEE Global Conference on Signal and Information Processin

Aspects of structural health and condition monitoring of offshore wind turbines

Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector

Zero-Shot Duet Singing Voices Separation with Diffusion Models

In recent studies, diffusion models have shown promise as priors for solving audio inverse problems. These models allow us to sample from the posterior distribution of a target signal given an observed signal by manipulating the diffusion process. However, when separating audio sources of the same type, such as duet singing voices, the prior learned by the diffusion process may not be sufficient to maintain the consistency of the source identity in the separated audio. For example, the singer may change from one to another occasionally. Tackling this problem will be useful for separating sources in a choir, or a mixture of multiple instruments with similar timbre, without acquiring large amounts of paired data. In this paper, we examine this problem in the context of duet singing voices separation, and propose a method to enforce the coherency of singer identity by splitting the mixture into overlapping segments and performing posterior sampling in an auto-regressive manner, conditioning on the previous segment. We evaluate the proposed method on the MedleyVox dataset and show that the proposed method outperforms the naive posterior sampling baseline. Our source code and the pre-trained model are publicly available at https://github.com/yoyololicon/duet-svs-diffusion.Comment: 9 pages, 1 figure. Published at Sound Demixing Workshop 202

Using the Audio Respiration Signal for Multimodal Discrimination of Expressive Movement Qualities

In this paper we propose a multimodal approach to distinguish between movements displaying three different expressive qualities: fluid, fragmented, and impulsive movements. Our approach is based on the Event Synchronization algorithm, which is applied to compute the amount of synchronization between two low-level features extracted from multimodal data. In more details, we use the energy of the audio respiration signal captured by a standard microphone placed near to the mouth, and the whole body kinetic energy estimated from motion capture data. The method was evaluated on 90 movement segments performed by 5 dancers. Results show that fragmented movements display higher average synchronization than fluid and impulsive movements

The Singing Tree : a novel interactive musical experience

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 105-107).by William David Oliver.M.S

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

On certain ‘tools’for research into the perception and creation of music and the complex ways in which they affect one another

Perception is a constructive mental process, which cannot be considered impersonally. Similarly, music cannot be cognised solely on the basis of its score, since its coming into being is strictly connected to the activation of human memory and sound imagination. The patterns that emerge from the sounds of heard music enable the listener to draw conclusions regarding the structures those sounds embody. However, such conclusions are accompanied by a degree of uncertainty, which concerns not just the perceived moment of the heard music, but also the way in which it is represented in the listener’s memory. Perception is an inferential, multi-layered, uncertain process, in which particular patterns seem more likely than others. Mental representations of those probabilities lie behind such essential musical phenomena as surprise, tension, expectation and pitch identification, which are fixed elements of theperception of music. The aim of the present article is to describe the essence of three selected types of music modelling, based on spectral anticipation (Shlomo Dubnov), based on memory (Rens Bod), and exploiting the dynamic character of music to obtain information (Samer Abdallah and Mark Plumbley). All these models take account of the element of uncertainty that accompanies the perception of music; hence they make use the foundations of information theory and statistical analysis as measurement ‘tools’. The use of these tools makes it possible to obtain numerical rates, which inform us of the degree of predictability of the musical structures being analysed. One crucial advantage of these methods is the possibility of evaluating them in respect to the use of real musical structures, deriving from actual music, and not abstract structures formed for the purposes of research. We obtain cognitive insight into the analysed music by employing methods of a mathematical provenance, and so we have the possibility of examining music whilst taking account of the role of the listener, but with the use of objectivised methods