Blind estimation of reverberation time in classrooms and hospital wards

This paper investigates blind Reverberation Time (RT) estimation in occupied classrooms and hospital wards. Measurements are usually made while these spaces are unoccupied for logistical reasons. However, occupancy can have a significant impact on the rate of reverberant decay. Recent work has developed a Maximum Likelihood Estimation (MLE) method which utilises only passively recorded speech and music signals, this enables measurements to be made while the room is in use. In this paper the MLE method is applied to recordings made in classrooms during lessons. Classroom occupancy levels differ for each lesson, therefore a model is developed using blind estimates to predict the RT for any occupancy level to within ±0.07s for the mid-frequency octave bands. The model is also able to predict the effective room and per person absorption area. Ambient sound recordings were also carried out in a number of rooms in two hospitals for a week. Hospital measurements are more challenging as the occurrence of free reverberant decay is rarer than in schools and the acoustic conditions may be non-stationary. However, by gaining recordings over a period of a week, estimates can be gained within ±0.07 s. These estimates are representative of the times when the room contains the highest acoustic absorption. In other words when curtains are drawn, there are many visitors or perhaps a window may be open

Studies on binaural and monaural signal analysis methods and applications

Sound signals can contain a lot of information about the environment and the sound sources present in it. This thesis presents novel contributions to the analysis of binaural and monaural sound signals. Some new applications are introduced in this work, but the emphasis is on analysis methods. The three main topics of the thesis are computational estimation of sound source distance, analysis of binaural room impulse responses, and applications intended for augmented reality audio. A novel method for binaural sound source distance estimation is proposed. The method is based on learning the coherence between the sounds entering the left and right ears. Comparisons to an earlier approach are also made. It is shown that these kinds of learning methods can correctly recognize the distance of a speech sound source in most cases. Methods for analyzing binaural room impulse responses are investigated. These methods are able to locate the early reflections in time and also to estimate their directions of arrival. This challenging problem could not be tackled completely, but this part of the work is an important step towards accurate estimation of the individual early reflections from a binaural room impulse response. As the third part of the thesis, applications of sound signal analysis are studied. The most notable contributions are a novel eyes-free user interface controlled by finger snaps, and an investigation on the importance of features in audio surveillance. The results of this thesis are steps towards building machines that can obtain information on the surrounding environment based on sound. In particular, the research into sound source distance estimation functions as important basic research in this area. The applications presented could be valuable in future telecommunications scenarios, such as augmented reality audio

An adaptive stereo basis method for convolutive blind audio source separation

NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in PUBLICATION, [71, 10-12, June 2008] DOI:neucom.2007.08.02

Structured Sparsity Models for Multiparty Speech Recovery from Reverberant Recordings

We tackle the multi-party speech recovery problem through modeling the acoustic of the reverberant chambers. Our approach exploits structured sparsity models to perform room modeling and speech recovery. We propose a scheme for characterizing the room acoustic from the unknown competing speech sources relying on localization of the early images of the speakers by sparse approximation of the spatial spectra of the virtual sources in a free-space model. The images are then clustered exploiting the low-rank structure of the spectro-temporal components belonging to each source. This enables us to identify the early support of the room impulse response function and its unique map to the room geometry. To further tackle the ambiguity of the reflection ratios, we propose a novel formulation of the reverberation model and estimate the absorption coefficients through a convex optimization exploiting joint sparsity model formulated upon spatio-spectral sparsity of concurrent speech representation. The acoustic parameters are then incorporated for separating individual speech signals through either structured sparse recovery or inverse filtering the acoustic channels. The experiments conducted on real data recordings demonstrate the effectiveness of the proposed approach for multi-party speech recovery and recognition.Comment: 31 page

Estimation of room acoustic parameters: the ACE challenge

Reverberation Time (T60) and Direct-to-Reverberant Ratio (DRR) are important parameters which together can characterize sound captured by microphones in non-anechoic rooms. These parameters are important in speech processing applications such as speech recognition and dereverberation. The values of T60 and DRR can be estimated directly from the Acoustic Impulse Response (AIR) of the room. In practice, the AIR is not normally available, in which case these parameters must be estimated blindly from the observed speech in the microphone signal. The Acoustic Characterization of Environments (ACE) Challenge aimed to determine the state-of-the-art in blind acoustic parameter estimation and also to stimulate research in this area. A summary of the ACE Challenge, and the corpus used in the challenge is presented together with an analysis of the results. Existing algorithms were submitted alongside novel contributions, the comparative results for which are presented in this paper. The challenge showed that T60 estimation is a mature field where analytical approaches dominate whilst DRR estimation is a less mature field where machine learning approaches are currently more successful

Blind estimation of room acoustic parameters from speech signals based on extended model of room impulse response

The speech transmission index (STI) and room acoustic parameters (RAPs), which are derived from a room impulse response (RIR), such as reverberation time and early decay time, are essential to assess speech transmission and to predict the listening difficulty in a sound field. Since it is difficult to measure RIR in daily occupied spaces, simultaneous blind estimation of STI and RAPs must be resolved as it is an imperative and challenging issue. This paper proposes a deterministic method for blindly estimating STI and five RAPs on the basis of an RIR stochastic model that approximates an unknown RIR. The proposed method formulates a temporal power envelope of a reverberant speech signal to obtain the optimal parameters for the RIR model. Simulations were conducted to evaluate STI and RAPs from observed reverberant speech signals. The root-mean-square errors between the estimated and ground-truth results were used to comparatively evaluate the proposed method with the previous method. The results showed that the proposed method can estimate STI and RAPs effectively without any training.Comment: 5-pages, 3 figures, 2 table

Acoustical comfort in university lecture halls: simulating the dynamic role of occupancy

The acoustic comfort in teaching environments is generally determined through requirements concerning reverberation time, speech intelligibility and HVAC noise. The presence of students reduces the reverberation time but concurrently increases the background noise, that undermines the focus of students and the vocal effort of teachers. A double set of measures were acquired in two university lecture halls in unoccupied and occupied state to investigate the consequences of occupancy variations. Acoustic simulations allowed to assess the dynamic effects of the occupancy detecting the differences of speech intelligibility. Predicting the student activity may return reliable outcomes improving the quality of lessons

Blind estimation of room acoustic parameters from speech and music signals

The acoustic character of a space is often quantified using objective room acoustic parameters. The measurement of these parameters is difficult in occupied conditions and thus measurements are usually performed when the space is un-occupied. This is despite the knowledge that occupancy can impact significantly on the measured parameter value. Within this thesis new methods are developed by which naturalistic signals such as speech and music can be used to perform acoustic parameter measurement. Adoption of naturalistic signals enables passive measurement during orchestral performances and spoken announcements, thus facilitating easy in-situ measurement. Two methods are described within this work; (1) a method utilising artificial neural networks where a network is taught to recognise acoustic parameters from received, reverberated signals and (2) a method based on the maximum likelihood estimation of the decay curve of the room from which parameters are then calculated. (1) The development of the neural network method focuses on a new pre-processor for use with music signals. The pre-processor utilises a narrow band filter bank with centre frequencies chosen based on the equal temperament scale. The success of a machine learning method is linked to the quality of the training data and therefore realistic acoustic simulation algorithms were used to generate a large database of room impulse responses. Room models were defined with realistic randomly generated geometries and surface properties; these models were then used to predict the room impulse responses. (2) In the second approach, a statistical model of the decay of sound in a room was further developed. This model uses a maximum likelihood (ML) framework to yield a number of decay curve estimates from a received reverberant signal. The success of the method depends on a number of stages developed for the algorithm; (a) a pre-processor to select appropriate decay phases for estimation purposes, (b) a rigorous optimisation algorithm to ensure the correct maximum likelihood estimate is found and (c) a method to yield a single optimum decay curve estimate from which the parameters are calculated. The ANN and ML methods were tested using orchestral music and speech signals. The ANN method tended to perform well when estimating the early decay time (EDT), for speech and music signals the error was within the subjective difference limens. However, accuracy was reduced for the reverberation time (Rt) and other parameters. By contrast the ML method performed well for Rt with results for both speech and music within the difference limens for reasonable (<4s) reverberation time. In addition reasonable accuracy was found for EDT, Clarity (C80), Centre time (Ts) and Deutichkeit (D). The ML method is also capable of producing accurate estimates of the binaural parameters Early Lateral Energy Fraction (LEF) and the late lateral strength (LG). A number of real world measurements were carried out in concert halls where the ML accuracy was shown to be sufficient for most parameters. The ML method has the advantage over the ANN method due to its truly blind nature (the ANN method requires a period of learning and is therefore semi-blind). The ML method uses gaps of silence between notes or utterances, when these silence regions are not present the method does not produce an estimate. Accurate estimation requires a long recording (hours of music or many minutes of speech) to ensure that at least some silent regions are present. This thesis shows that, given a sufficiently long recording, accurate estimates of many acoustic parameters can be obtained directly from speech and music. Further extensions to the ML method detailed in this thesis combine the ML estimated decay curve with cepstral methods which detect the locations of early reflections. This improves the accuracy of many of the parameter estimates.EThOS - Electronic Theses Online ServiceGBUnited Kingdo