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

Joint estimation of reverberation time and early-to-late reverberation ratio from single-channel speech signals

The reverberation time (RT) and the early-to-late reverberation ratio (ELR) are two key parameters commonly used to characterize acoustic room environments. In contrast to conventional blind estimation methods that process the two parameters separately, we propose a model for joint estimation to predict the RT and the ELR simultaneously from single-channel speech signals from either full-band or sub-band frequency data, which is referred to as joint room parameter estimator (jROPE). An artificial neural network is employed to learn the mapping from acoustic observations to the RT and the ELR classes. Auditory-inspired acoustic features obtained by temporal modulation filtering of the speech time-frequency representations are used as input for the neural network. Based on an in-depth analysis of the dependency between the RT and the ELR, a two-dimensional (RT, ELR) distribution with constrained boundaries is derived, which is then exploited to evaluate four different configurations for jROPE. Experimental results show that-in comparison to the single-task ROPE system which individually estimates the RT or the ELR-jROPE provides improved results for both tasks in various reverberant and (diffuse) noisy environments. Among the four proposed joint types, the one incorporating multi-task learning with shared input and hidden layers yields the best estimation accuracies on average. When encountering extreme reverberant conditions with RTs and ELRs lying beyond the derived (RT, ELR) distribution, the type considering RT and ELR as a joint parameter performs robustly, in particular. From state-of-the-art algorithms that were tested in the acoustic characterization of environments challenge, jROPE achieves comparable results among the best for all individual tasks (RT and ELR estimation from full-band and sub-band signals)

Exploring auditory-inspired acoustic features for room acoustic parameter estimation from monaural speech

Room acoustic parameters that characterize acoustic environments can help to improve signal enhancement algorithms such as for dereverberation, or automatic speech recognition by adapting models to the current parameter set. The reverberation time (RT) and the early-to-late reverberation ratio (ELR) are two key parameters. In this paper, we propose a blind ROom Parameter Estimator (ROPE) based on an artificial neural network that learns the mapping to discrete ranges of the RT and the ELR from single-microphone speech signals. Auditory-inspired acoustic features are used as neural network input, which are generated by a temporal modulation filter bank applied to the speech time-frequency representation. ROPE performance is analyzed in various reverberant environments in both clean and noisy conditions for both fullband and subband RT and ELR estimations. The importance of specific temporal modulation frequencies is analyzed by evaluating the contribution of individual filters to the ROPE performance. Experimental results show that ROPE is robust against different variations caused by room impulse responses (measured versus simulated), mismatched noise levels, and speech variability reflected through different corpora. Compared to state-of-the-art algorithms that were tested in the acoustic characterisation of environments (ACE) challenge, the ROPE model is the only one that is among the best for all individual tasks (RT and ELR estimation from fullband and subband signals). Improved fullband estimations are even obtained by ROPE when integrating speech-related frequency subbands. Furthermore, the model requires the least computational resources with a real time factor that is at least two times faster than competing algorithms. Results are achieved with an average observation window of 3 s, which is important for real-time applications

Efficient and Robust Methods for Audio and Video Signal Analysis

This thesis presents my research concerning audio and video signal processing and machine learning. Specifically, the topics of my research include computationally efficient classifier compounds, automatic speech recognition (ASR), music dereverberation, video cut point detection and video classification.Computational efficacy of information retrieval based on multiple measurement modalities has been considered in this thesis. Specifically, a cascade processing framework, including a training algorithm to set its parameters has been developed for combining multiple detectors or binary classifiers in computationally efficient way. The developed cascade processing framework has been applied on video information retrieval tasks of video cut point detection and video classification. The results in video classification, compared to others found in the literature, indicate that the developed framework is capable of both accurate and computationally efficient classification. The idea of cascade processing has been additionally adapted for the ASR task. A procedure for combining multiple speech state likelihood estimation methods within an ASR framework in cascaded manner has been developed. The results obtained clearly show that without impairing the transcription accuracy the computational load of ASR can be reduced using the cascaded speech state likelihood estimation process.Additionally, this thesis presents my work on noise robustness of ASR using a nonnegative matrix factorization (NMF) -based approach. Specifically, methods for transformation of sparse NMF-features into speech state likelihoods has been explored. The results reveal that learned transformations from NMF activations to speech state likelihoods provide better ASR transcription accuracy than dictionary label -based transformations. The results, compared to others in a noisy speech recognition -challenge show that NMF-based processing is an efficient strategy for noise robustness in ASR.The thesis also presents my work on audio signal enhancement, specifically, on removing the detrimental effect of reverberation from music audio. In the work, a linear prediction -based dereverberation algorithm, which has originally been developed for speech signal enhancement, was applied for music. The results obtained show that the algorithm performs well in conjunction with music signals and indicate that dynamic compression of music does not impair the dereverberation performance

ATHENA Research Book, Volume 2

ATHENA European University is an association of nine higher education institutions with the mission of promoting excellence in research and innovation by enabling international cooperation. The acronym ATHENA stands for Association of Advanced Technologies in Higher Education. Partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal and Slovenia: University of Orléans, University of Siegen, Hellenic Mediterranean University, Niccolò Cusano University, Vilnius Gediminas Technical University, Polytechnic Institute of Porto and University of Maribor. In 2022, two institutions joined the alliance: the Maria Curie-Skłodowska University from Poland and the University of Vigo from Spain. Also in 2022, an institution from Austria joined the alliance as an associate member: Carinthia University of Applied Sciences. This research book presents a selection of the research activities of ATHENA University's partners. It contains an overview of the research activities of individual members, a selection of the most important bibliographic works of members, peer-reviewed student theses, a descriptive list of ATHENA lectures and reports from individual working sections of the ATHENA project. The ATHENA Research Book provides a platform that encourages collaborative and interdisciplinary research projects by advanced and early career researchers