Department Publications

In this paper, we consider the problem of recovering desired sound source signals from on-board microphone recordings on a noisy drone. Enhancement of source signal degraded by drone noise is considered to be a difficult task due to the strong noise generated from its motors and propellers causing an extremely low signal-to-drone noise ratio (S D ‾ NR). We propose a solution (i) by combining the widely known multichannel Wiener filter (MWF) to remove drone noise from microphone recordings, and (ii) further reduction of residual noise using a Gaussian mixture model (GMM) based dual-stage parametric Wiener filter (WF). The method exploits known statistics of motor current-specific drone noise. This combination of techniques to the specific context of signal enhancement for drone audition is applicable to irregular microphone arrays embedded on a drone enabling realistic integration to most drones. We …

Perception of sounds and speech involves structures in the auditory brainstem that rapidly process ongoing auditory stimuli. The role of these structures in speech processing can be investigated by measuring their electrical activity using scalp-mounted electrodes. However, typical analysis methods involve averaging neural responses to many short repetitive stimuli that bear little relevance to daily listening environments. Recently, subcortical responses to more ecologically relevant continuous speech were detected using linear encoding models. These methods estimate the temporal response function (TRF), which is a regression model that minimises the error between the measured neural signal and a predictor derived from the stimulus. Using predictors that model the highly non-linear peripheral auditory system may improve linear TRF estimation accuracy and peak detection. Here, we compare predictors from both simple and complex peripheral auditory models for estimating brainstem TRFs on electroencephalography (EEG) data from 24 participants listening to continuous speech. We also investigate the data length required for estimating subcortical TRFs, and find that around 12 minutes of data is sufficient for clear wave V peaks (>3 dB SNR) to be seen in nearly all participants. Interestingly, predictors derived from simple filterbank-based models of the peripheral auditory system yield TRF wave V peak SNRs that are not significantly different from those estimated using a complex model of the auditory nerve, provided that the nonlinear effects of adaptation in the auditory system are appropriately modelled. Crucially, computing predictors …

This paper presents several machine learning methods and highlights the most effective one for detecting elephant rumbles in infrasonic seismic signals. The design and implementation of electronic circuitry to amplify, filter, and digitize the seismic signals captured through geophones are presented. The process converts seismic rumbles to a spectrogram and the existing methods of spectrogram feature extraction and appropriate machine learning algorithms are compared on their merit for automatic seismic rumble identification. A novel method of denoising the spectrum that leads to enhanced accuracy in identifying seismic rumbles is presented. It is experimentally found that the combination of the Mel-frequency cepstral coefficient (MFCC) feature extraction method and the ridge classifier machine learning algorithm give the highest accuracy of 97% in detecting infrasonic elephant rumbles hidden in seismic signals. The trained machine learning algorithm can run quite efficiently on general-purpose embedded hardware such as a Raspberry Pi, hence the method provides a cost-effective and scalable platform to develop a tool to remotely localize elephants, which would help mitigate the human–elephant conflict.

The auditory brainstem response (ABR) is a valuable clinical tool for objective hearing assessment, which is conventionally detected by averaging neural responses to thousands of short stimuli. Progressing beyond these unnatural stimuli, brainstem responses to continuous speech presented via earphones have been recently detected using linear temporal response functions (TRFs). Here, we extend earlier studies by measuring subcortical responses to continuous speech presented in the sound-field, and assess the amount of data needed to estimate brainstem TRFs. Electroencephalography (EEG) was recorded from 24 normal hearing participants while they listened to clicks and stories presented via earphones and loudspeakers. Subcortical TRFs were computed after accounting for non-linear processing in the auditory periphery by either stimulus rectification or an auditory nerve model. Our results …

The Relative Transfer Matrix (ReTM), recently introduced as a generalization of the relative transfer function for multiple sources and multiple microphones, shows promising performance when applied to speech denoising and speaker separation in a noisy reverberant room. This work utilizes the ReTM to propose a novel framework for drone noise suppression. Difficulties in noise cancellation for drone audition applications arise due to its self-generated noise that causes an adverse noisy environment of low Signal-to-Drone Noise Ratio (SDNR) levels. In this paper, we divide the drone on-board microphones into two multichannel groups to approximately estimate the drone noise from one group to the other with known drone noise ReTM for denoising. We demonstrate the ReTM spatial mapping ability in both indoor and outdoor experiments using hovering drone on-board microphone recordings with low average …

The auditory brainstem response (ABR) is a measure of subcortical activity in response to auditory stimuli. The wave V peak of the ABR depends on the stimulus intensity level, and has been widely used for clinical hearing assessment. Conventional methods estimate the ABR average electroencephalography (EEG) responses to short unnatural stimuli such as clicks. Recent work has moved toward more ecologically relevant continuous speech stimuli using linear deconvolution models called temporal response functions (TRFs). Investigating whether the TRF waveform changes with stimulus intensity is a crucial step toward the use of natural speech stimuli for hearing assessments involving subcortical responses. Here, we develop methods to estimate level-dependent subcortical TRFs using EEG data collected from 21 participants listening to continuous speech presented at 4 different intensity levels. We find that …

Estimating the Relative Transfer Functions (ReTFs) of multiple speakers in a noisy reverberant environment are beneficial to signal processing applications of acoustic scene analysis, denoising, dereverberation, signal enhancement, and separation. Whilst most audio applications exploit the covariance matrix structure of multichannel recordings, estimating ReTF in the presence of simultaneously active multiple speakers is still considered to be a challenging problem. In this paper, we propose a novel method for estimating the ReTF using the relative transfer matrix (ReTM) of multiple microphones for multi-talker scenarios, which is suitable for noisy reverberant rooms. The method is based on the noise-only ReTM, and calculates the covariance matrices of (i) the first speaker and noise, and (ii) both speakers and noise at two microphone groups to reconstruct the ReTF of the second speaker. We demonstrate the …

With the recent emergence of virtual reality, augmented reality, and mixed-reality technologies, considerable attention has been received to integrate spatial audio recording capabilities into these devices. Extracting higher order spherical harmonics from the recordings is not always possible due to complex scattering effects from the arbitrary-shaped devices, which motivates the need for modeling the acoustic properties beyond free-field convention. In this paper, we propose modeling the wearable device-related transfer function (WDRTF) that incorporates scattering, and diffraction from the device and wearer’s body to an incident soundfield. We adopt the spherical wave model to translate the measurements into microphone alignment, by exploiting both magnitude and phase corrections. We use a discrete set of measurements by multiple circular arrays for efficiently estimating the WDRTF coefficients. We illustrate …

Multichannel Wiener filter (MWF), commonly used as a spatial filter for noise reduction, exhibits promising performance due to its robustness in multi-channel setups. Additionally, the preservation of spatial cues at the MWF is usually a favorable property that contributes to sound source localization. Previous works have shown that the MWF can be used in drone noise suppression for acoustic sensing tasks in drone audition. In this paper, we analyze the preservation of spatial cues at the MWF from on-board microphone recordings on a drone at very low signal-to-drone noise ratio (SDNR) levels. The analysis builds upon the relative transfer function (ReTF), and explores the spatial information of the desired sound sources such as human speakers and birds through experimental recordings from simulated and real-life environments. We also demonstrate the effectiveness of the preservation of spatial cues at the MWF in an acoustic localization application

The auditory brainstem response (ABR) is a measure of subcortical activity in response to auditory stimuli. The wave V peak of the ABR depends on stimulus intensity level, and has been widely used for clinical hearing assessment. Conventional methods to estimate the ABR average electroencephalography (EEG) responses to short unnatural stimuli such as clicks. Recent work has moved towards more ecologically relevant continuous speech stimuli using linear deconvolution models called Temporal Response Functions (TRFs). Investigating whether the TRF waveform changes with stimulus intensity is a crucial step towards the use of natural speech stimuli for hearing assessments involving subcortical responses. Here, we develop methods to estimate level-dependent subcortical TRFs using EEG data collected from 21 participants listening to continuous speech presented at 4 different intensity levels. We find that level-dependent changes can be detected in the wave V peak of the subcortical TRF for almost all participants, and are consistent with level-dependent changes in click-ABR wave V. We also investigate the most suitable peripheral auditory model to generate predictors for level-dependent subcortical TRFs and find that simple gammatone filterbanks perform the best. Additionally, around 6 minutes of data may be sufficient for detecting level-dependent effects and wave V peaks above the noise floor for speech segments with higher intensity. Finally, we show a proof-of-concept that level dependent subcortical TRFs can be detected even for the inherent intensity fluctuations in natural continuous speech. Visual abstract Significance …

Making drones talk is the next step in Human-Unmanned Aerial Vehicle interactions by installing an on-board loudspeaker array. However, due to the drone being loud, it creates an adverse noisy environment of very low signal-to-drone noise ratio (SDNR) levels (less than -10 dB) making it difficult to project a perceptual soundfield to a desired location on the ground. In this paper, we present a preliminary study on developing a sparsity-based interior response model of a commercial loudspeaker that can be used to modify the speech signals before playback through the loudspeaker. We incorporate the ground reflections into the incident response model of the real loudspeaker to simulate the outdoor environment. We validate the proposed model using soundfield recording over a larger region for low frequencies up to 114 Hz.

This thesis explores the acoustic signal processing on drones which can enhance the drone audition functions for search and rescue missions, wildlife monitoring, and video recording for media and filming industries. With the recent emergence of drone-based applications and services, microphone arrays are beginning to be mounted to drones. However, audio capturing using drone-mounted microphone arrays leads to very low signal-to-drone noise ratio (SDNR) recordings (below negative 10 dB) due to the drone-generated motor and propeller noise signals. Hence, this causes a highly adverse noisy environment and degrades the quality and intelligibility of the recorded audio signals. Therefore, there is a growing interest in deriving new signal processing algorithms for the future deployment of the drone audition. To this end, we develop novel methods for spatial soundfield modelling, and spatial audio processing using a microphone array embedded into the body of drones for sound source localisation and audio signal enhancement. To a lesser extent, drone noise characterisation is also studied. We propose modelling of the drone noise harmonics as current to noise transfer function by taking the significant harmonics of the drone noise signature. The model is derived from the experimental measurements of a single motor propeller combination, and validated using extensive error analysis. A cross-correlation-based DOA estimation method with drone noise angular spectrum subtraction is derived for acoustic source localisation. Our approach is to exploit the underlying spatial characteristics of drone noise to mitigate the noise emission by …

With the recent emergence of virtual reality, augmented reality, and mixed-reality technologies, considerable attention has been received to integrate spatial audio recording capabilities into these devices. Extracting higher order spherical harmonics from the recordings is not always possible due to complex scattering effects from the arbitrary-shaped devices, which motivates the need for modeling the acoustic properties beyond free-field convention. In this paper, we propose modeling the wearable device-related transfer function (WDRTF) that incorporates scattering, and diffraction from the device and wearer's body to an incident soundfield. We adopt the spherical wave model to translate the measurements into microphone alignment, by exploiting both magnitude and phase corrections. We use a discrete set of measurements by multiple circular arrays for efficiently estimating the WDRTF coefficients. We illustrate the WDRTF modeling procedure from a head-worn microphone array and obtain accurate reconstruction performance over frequency and 3D directions.

The Relative Transfer Matrix (ReTM), recently introduced as a generalization of the relative transfer function for multiple sources and multiple microphones, shows promising performance when applied to speech denoising and speaker separation in a noisy reverberant room. This work utilizes the ReTM to propose a novel framework for drone noise suppression. Difficulties in noise cancellation for drone audition applications arise due to its self-generated noise that causes an adverse noisy environment of low Signal-to-Drone Noise Ratio (SDNR) levels. In this paper, we divide the drone on-board microphones into two multichannel groups to approximately estimate the drone noise from one group to the other with known drone noise ReTM for denoising. We demonstrate the ReTM spatial mapping ability in both indoor and outdoor experiments using hovering drone on-board microphone recordings with low average magnitude spectrum error. Finally, we validate the method in a real-life environment for source signal enhancement over different SDNR conditions and offer both improved speech intelligibility and signal-to-distortion ratio.