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  • Conference paper
    Evers C, Moore AH, Naylor PA, 2016,

    Acoustic simultaneous localization and mapping (A-SLAM) of a moving microphone array and its surrounding speakers

    , 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 6-10, ISSN: 1520-6149

    Acoustic scene mapping creates a representation of positions of audio sources such as talkers within the surrounding environment of a microphone array. By allowing the array to move, the acoustic scene can be explored in order to improve the map. Furthermore, the spatial diversity of the kinematic array allows for estimation of the source-sensor distance in scenarios where source directions of arrival are measured. As sound source localization is performed relative to the array position, mapping of acoustic sources requires knowledge of the absolute position of the microphone array in the room. If the array is moving, its absolute position is unknown in practice. Hence, Simultaneous Localization and Mapping (SLAM) is required in order to localize the microphone array position and map the surrounding sound sources. In realistic environments, microphone arrays receive a convolutive mixture of direct-path speech signals, noise and reflections due to reverberation. A key challenge of Acoustic SLAM (a-SLAM) is robustness against reverberant clutter measurements and missing source detections. This paper proposes a novel bearing-only a-SLAM approach using a Single-Cluster Probability Hypothesis Density filter. Results demonstrate convergence to accurate estimates of the array trajectory and source positions.

  • Journal article
    Neeld T, Eaton J, Naylor PA, Shipworth Det al., 2016,

    A novel method of determining events in combination gas boilers: Assessing the feasibility of a passive acoustic sensor

    , Building and Environment, Vol: 100, Pages: 1-9, ISSN: 0360-1323

    To assess the impact of interventions designed to reduce residential space heating demand, investigators must be armed with field-trial applicable techniques that accurately measure space heating energy use. This study assesses the feasibility of using a passive acoustic sensor to detect gas consumption events in domestic combination gas-fired boilers (C-GFBs). The investigation has shown, for the C-GFB investigated, the following events are discernible using a passive acoustic sensor: demand type (hot water or central heating); boiler ignition time; and pre-mix fan motor speed. A detection algorithm was developed to automatically identify demand type and burner ignition time with accuracies of 100% and 97% respectfully. Demand type was determined by training a naive Bayes classifier on 20 features of the acoustic profile at the start of a demand event. Burner ignition was determined by detecting low frequency (5–10 Hz) pressure pulsations produced during ignition. The acoustic signatures of the pre-mix fan and circulation-pump were identified manually. Additional work is required to detect burner duration, deal with detection in the presence of increased noise and expand the range of boilers investigated. There are considerable implications resulting from the widespread use of such techniques on improving understanding of space heating demand.

  • Conference paper
    Doire CSJ, Brookes DM, Naylor PA, De Sena E, van Waterschoot T, Jensen SHJet al., 2016,

    Acoustic Environment Control: Implementation of a Reverberation Enhancement System

    , AES 60th International Conference: DREAMS (Dereverberation and Reverberation of Audio, Music, and Speech)

    Reverberation enhancement systems allow the active control of the acoustic environment. They are subject to instability issues due to acoustic feedback, and are often installed permanently in large halls, sometimes at great cost. In this paper, we explore the possibility of implementing a cost-effective reverberation enhancement system to control the acoustics of typical rooms using a combination of spatial filtering, automatic calibration, adaptive notch filters, howling detection and manual adjustments. The effectiveness of the system is then tested inside a small soundproof booth.

  • Journal article
    Parada PP, Sharma D, Lainez J, Barreda D, van Waterschoot T, Naylor PAet al., 2016,

    A single-channel non-intrusive C50 estimator correlated with speech recognition performance

    , IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol: 24, Pages: 719-732, ISSN: 2329-9304
  • Conference paper
    Evers C, Moore A, Naylor P, 2016,

    Towards Informative Path Planning for Acoustic SLAM

    , DAGA 2016

    Acoustic scene mapping is a challenging task as microphonearrays can often localize sound sources only interms of their directions. Spatial diversity can be exploitedconstructively to infer source-sensor range whenusing microphone arrays installed on moving platforms,such as robots. As the absolute location of a moving robotis often unknown in practice, Acoustic SimultaneousLocalization And Mapping (a-SLAM) is required in orderto localize the moving robot’s positions and jointlymap the sound sources. Using a novel a-SLAM approach,this paper investigates the impact of the choice of robotpaths on source mapping accuracy. Simulation results demonstratethat a-SLAM performance can be improved byinformatively planning robot paths.

  • Conference paper
    Zhang W, Naylor PA, He Z, Zhang Yet al., 2016,

    ON THE EVALUATION OF MULTICHANNEL BLIND SYSTEM IDENTIFICATION FROM THE VIEWPOINT OF SYSTEM EQUALIZATION

    , 15th International Workshop on Acoustic Signal Enhancement (IWAENC), Publisher: IEEE
  • Conference paper
    Hafezi S, Moore AH, Naylor PA, 2016,

    3D ACOUSTIC SOURCE LOCALIZATION IN THE SPHERICAL HARMONIC DOMAIN BASED ON OPTIMIZED GRID SEARCH

    , 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 415-419, ISSN: 1520-6149
  • Conference paper
    Zahedi A, Ostergaard J, Jensen SH, Naylor P, Bech Set al., 2016,

    On Perceptual Audio Compression with Side Information at the Decoder

    , Data Compression Conference (DCC), Publisher: IEEE, Pages: 456-465, ISSN: 1068-0314
  • Conference paper
    Cauchi B, Javed H, Gerkmann T, Doclo S, Goetze S, Naylor Pet al., 2016,

    PERCEPTUAL AND INSTRUMENTAL EVALUATION OF THE PERCEIVED LEVEL OF REVERBERATION

    , 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 629-633, ISSN: 1520-6149
  • Conference paper
    Zhang W, Naylor PA, 2016,

    AN ITERATIVE METHOD FOR EQUALIZATION OF MULTICHANNEL ACOUSTIC SYSTEMS ROBUST TO SYSTEM IDENTIFICATION ERRORS

    , 15th International Workshop on Acoustic Signal Enhancement (IWAENC), Publisher: IEEE
  • Conference paper
    Cauchi B, Santos JF, Siedenburg K, Falk TH, Naylor PA, Doclo S, Goetze Set al., 2016,

    Predicting the quality of processed speech by combining modulation-based features and model trees

    , Pages: 180-184

    Many signal processing methods have been proposed to improve the quality of speech recorded in the presence of noise and reverberation. The evaluation of these methods either requires the use of perceptual measures, i.e. listening tests, or instrumental measures. Perceptual measures are typically more reliable but are quite costly and time-consuming. On the other hand, instrumental measures may correlate poorly with the perceived speech quality. In this paper we propose to train an instrumental measure, combining modulation-based features and model trees, on the basis of perceptual scores obtained on a small corpus of speech data that has been processed by a combination of beamforming and spectral postfiltering. For evaluation purposes the resulting measure is then applied to a larger corpus. Results show that the use of model trees to train the predicting function of an instrumental measure increases its correlation with perceptual scores.

  • Conference paper
    Hu M, Sharma D, Doclo S, Brookes M, Naylor PAet al., 2016,

    Blind adaptive SIMO acoustic system identification using a locally optimal step-size

    , 60th AES International Conference on Dereverberation and Reverberation of Audio, Music, and Speech (DREAMS), Publisher: AUDIO ENGINEERING SOC INC
  • Conference paper
    Cauchi B, Gerkmann T, Doclo S, Naylor PA, Goetze Set al., 2016,

    Spectrally and spatially informed noise suppression using beamforming and convolutive NMF

    , 60th AES International Conference on Dereverberation and Reverberation of Audio, Music, and Speech (DREAMS), Publisher: AUDIO ENGINEERING SOC INC
  • Conference paper
    Antonello N, De Sena E, Moonen M, Naylor PA, van Waterschoot Tet al., 2016,

    Sound field control in a reverberant room using the Finite Difference Time Domain method

    , 60th AES International Conference on Dereverberation and Reverberation of Audio, Music, and Speech (DREAMS), Publisher: AUDIO ENGINEERING SOC INC
  • Conference paper
    Parada PP, Sharma D, Naylor PA, van Waterschoot Tet al., 2016,

    Analysis of prediction intervals for non-intrusive estimation of speech clarity index

    , 60th AES International Conference on Dereverberation and Reverberation of Audio, Music, and Speech (DREAMS), Publisher: AUDIO ENGINEERING SOC INC
  • Conference paper
    Javed HA, Moore AH, Naylor PA, 2016,

    SPHERICAL HARMONIC RAKE RECEIVERS FOR DEREVERBERATION

    , 15th International Workshop on Acoustic Signal Enhancement (IWAENC), Publisher: IEEE
  • Conference paper
    De Sena E, Kaplanis N, Naylor PA, van Waterschoot Tet al., 2016,

    Large-scale auralised sound localisation experiment

    , 60th AES International Conference on Dereverberation and Reverberation of Audio, Music, and Speech (DREAMS), Publisher: AUDIO ENGINEERING SOC INC
  • Conference paper
    Eaton J, Gaubitch ND, Moore AH, Naylor PAet al., 2015,

    The ACE Challenge - corpus description and performance evaluation

    , WASPAA, Publisher: IEEE

    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.

  • Conference paper
    Eaton J, Naylor PA, 2015,

    Direct-to-Reverberant ratio estimation on the ACE corpus using a Two-channel beamformer

    , arXiv, ACE Challenge Workshop, a satellite event of IEEE-WASPAA, Publisher: arXiv

    Direct-to-Reverberant Ratio (DRR) is an important measure for characterizing the properties of a room. The recently proposed DRR Estimation using a Null-Steered Beamformer (DENBE) algorithm was originally tested on simulated data where noise was artificially added to the speech after convolution with impulse responses simulated using the image-source method. This paper evaluates the performance of this algorithm on speech convolved with measured impulse responses and noise using the Acoustic Characterization of Environments (ACE) Evaluation corpus. The fullband DRR estimation performance of the DENBE algorithm exceeds that of the baselines in all Signal-to-Noise Ratios (SNRs) and noise types. In addition, estimation of the DRR in one third-octave ISO frequency bands is demonstrated.

  • Conference paper
    Eaton J, Naylor PA, 2015,

    Reverberation time estimation on the ACE corpus using the SDD method

    , arXiv, ACE Challenge Workshop, a satellite event of IEEE-WASPAA, Publisher: arXiv

    Reverberation Time ($T_60$) is an important measure for characterizing the properties of a room. The author’s $T_60$ estimation algorithm was previously tested on simulated data where the noise is artificially added to the speech after convolution with a impulse responses simulated using the image method. We test the algorithm on speech convolved with real recorded impulse responses and noise from the same rooms from the Acoustic Characterization of Environments (ACE) corpus and achieve results comparable results to those using simulated data.

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