Abstract-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.

Knowledge of the Direct-to-Reverberant Ratio (DRR) and Reverberation Time (T60) can be used to better perform speech and audio processing such as dereverberation. Established methods compute these parameters from measured Acoustic Impulse Responses (AIRs). However, in many practical situations the AIR is not available and the parameters must be estimated non-intrusively directly from noisy speech or audio signals. The Acoustic Characterization of Environments (ACE) Challenge is a competition to identify the most promising non-intrusive DRR and T60 estimation methods using real noisy reverberant speech. We describe the ACE corpus comprising multi-channel AIRs, and multi-channel noise including ambient, fan and babble noise recorded in the same environment as the measured AIRs, along with the corresponding DRR and T60 measurements. The evaluation methodology is discussed and comparative results are shown.

No abstract

No abstract

No abstract

We present a reverberant speech enhancement algorithm which, operating on the linear prediction residual of spatially averaged multimicrophone observations, utilizes temporal averaging of neighbouring larynx cycles. The enhanced larynx cycles are used to design an equalization filter, which is applied in order to dereverberate both voiced and unvoiced speech. The DYPSA algorithm is employed and evaluated for larynx cycle segmentation of reverberant speech. Simulation results show reverberation reduction with up to 5 dB in terms of segmental signal-to-reverberant ratio and 0.34 in terms of normalized Bark spectral distortion score.

Reverberation Time (T60) is an important measure of the acoustic properties of a room. It can provide information about the acoustic environment, the intelligibility, and quality of speech recorded in the room, and help improve the performance of speech processing algorithms with reverberant speech. Where the acoustic impulse response of the room is not available, the T60 must be estimated nonintrusively from reverberant speech. State-of-the-art non-intrusive T60 estimators have been shown to be strongly biased in the presence of noise. We describe a novel T60 estimation algorithm based on spectral decay distributions that provides robustness to additive noise for a range of realistic noise types for signal-to-noise ratios in the range 0 to 35 dB and T60s between 200 and 950 ms. The proposed method also has much reduced computational cost.

Hands-free speech input is required in many modern telecommunication applications that employ autoregressive (AR) techniques such as linear predictive coding. When the hands-free input is obtained in enclosed reverberant spaces such as typical office rooms, the speech signal is distorted by the room transfer function. This paper utilizes theoretical results from statistical room acoustics to analyze the AR modeling of speech under these reverberant conditions. Three cases are considered: (i) AR coefficients calculated from a single observation; (ii) AR coefficients calculated jointly from an M-channel observation (M > 1); and (iii) AR coefficients calculated from the output of a delay-and sum beamformer. The statistical analysis, with supporting simulations, shows that the spatial expectation of the AR coefficients for cases (i) and (ii) are approximately equal to those from the original speech, while for case (iii) there is a discrepancy due to spatial correlation between the microphones which can be significant. It is subsequently demonstrated that at each individual source-microphone position (without spatial expectation), the M-channel AR coefficients from case (ii) provide the best approximation to the clean speech coefficients when microphones are closely spaced (<0.3m).