Data-Driven Wind Speed Estimation Using Multiple Microphones - Mirabilii, Daniele; Lakshminarayana, Kishor Kayyar; Mack, Wolfgang; Habets, Emanuël (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2020)
A deep neural network (DNN) based approach for estimating the speed of airflows using closely-spaced microphones is proposed. The spatial characteristics of wind noise measured with a smallaperture array are exploited, i.e., the low-frequency spatial coherence of wind noise signals is used as an input feature. The output is an estimate of the wind speed averaged over a specific time interval. The DNN is trained using synthetic wind noise, which overcomes the time-consuming data collection and allows to isolate wind noise from different acoustic sources. The dataset used for testing comprises wind noise measured outdoors with a circular linear array and a ground truth obtained using an ultrasonic anemometer. The obtained model is applied to generated and measured wind noise. The performance of the proposed method is assessed across a wide range of wind speeds and directions, using different time resolutions.
CRIS-Team Scopus Importer:2021-04-30
Keywords: Corcos model; multi-channel; Wind noise; wind speed

DIRECTION PRESERVING WIND NOISE REDUCTION OF B-FORMAT SIGNALS - Herzog, Adrian; Mirabilii, Daniele; Habets, Emanuël (2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), 2021)
CRIS-Team WoS Importer:2021-11-26

Generating coherence-constrained multisensor signals using balanced mixing and spectrally smooth filters - Mirabilii, Daniele; Schlecht, Sebastian J.; Habets, Emanuël (Journal of the Acoustical Society of America, 2021)
The spatial properties of a noise field can be described by a spatial coherence function. Synthetic multichannel noise signals exhibiting a specific spatial coherence can be generated by properly mixing a set of uncorrelated, possibly non-stationary, signals. The mixing matrix can be obtained by decomposing the spatial coherence matrix. As proposed in a widely used method, the factorization can be performed by means of a Choleski or eigenvalue decomposition. In this work, the limitations of these two methods are discussed and addressed. In particular, specific properties of the mixing matrix are analyzed, namely, the spectral smoothness and the mix balance. The first quantifies the mixing matrix-filters variation across frequency and the second quantifies the number of input signals that contribute to each output signal. Three methods based on the unitary Procrustes solution are proposed to enhance the spectral smoothness, the mix balance, and both properties jointly. A performance evaluation confirms the improvements of the mixing matrix in terms of objective measures. Furthermore, the evaluation results show that the error between the target and the generated coherence is lowered by increasing the spectral smoothness of the mixing matrix.
CRIS-Team Scopus Importer:2021-03-19

Multi-channel wind noise reduction using the Corcos model - Mirabilii, Daniele; Habets, Emanuël (Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2019)

On the difference-to-sum power ratio of speech and wind noise based on the corcos model - Mirabilii, Daniele; Habets, Emanuël (Proc. of the International Conference on the Science of Electrical Engineering, 2018)

Simulating multi-channel wind noise based on the Corcos model - Mirabilii, Daniele; Habets, Emanuël (Proc. of the International Workshop on Acoustic Signal Enhancement (IWAENC), 2018)
Keywords: Multi-channel; Corcos model; Wind noise

Simulating Wind Noise with Airflow Speed-Dependent Characteristics - Mirabilii, Daniele; Lodermeyer, Alexander; Czwielong, Felix; Becker, Stefan; Habets, Emanuël (International Workshop on Acoustic Signal Enhancement, IWAENC 2022 - Proceedings, 2022)
Generating noise samples is crucial in developing and testing noise reduction algorithms or training deep learning models. This work proposes a wind noise generation model with airflow speed-dependent features. A linear predictive analysis of wind noise measured in a wind tunnel at different flow velocities was carried out. This analysis showed that temporal and spectral features depend on the flow speed. The prediction residual's statistics and the filter coefficients are first extracted and then modeled based on the flow speed. The obtained models are then combined to synthetically generate wind noise given a time-varying flow speed profile as input, in contrast to an existing framework where temporal and spectral features were assumed speed-invariant. A subjective evaluation is carried out to assess the perceptual authenticity of the generated noise compared to the existing method.
CRIS-Team Scopus Importer:2022-11-25
Keywords: analysis; synthesis; Wind noise

Spatial Coherence-Aware Multi-Channel Wind Noise Reduction - Mirabilii, Daniele; Habets, Emanuël (IEEE/ACM Transactions on Audio, Speech and Language Processing, 2020)
Outdoor recording is particularly challenging in the presence of wind, which induces highly non-stationary noise in the microphone signals. To enhance a desired signal, e.g., speech, a dedicated noise reduction processing is required. The reduction is usually performed by estimating an unknown set of parameters, e.g., the noise and the speech power spectral densities (PSDs). In contrast to the commonly used assumption of uncorrelated wind noise in multi-channel recordings, we assume the spatial correlation of wind noise contributions to be non-zero at low frequencies when closely-spaced microphones are employed. In our earlier work, we assumed that the spatial coherence was known, i.e., given by a model which depends on the free-field speed and direction of the air stream. As these quantities are unknown in practice, we propose in this work a method to recursively estimate the spatial coherence matrix based on the microphone observations. In addition, we prove the equivalence of two recently developed noise PSD estimation methods when uncorrelated wind noise is assumed, and we propose an approximation of both estimators which is independent of the propagation vector of the speech source at sufficiently low frequency and for a small inter-microphone distance. An evaluation in terms of improvements in speech quality, signal-to-noise ratio and intelligibility is carried out using both simulated and measured wind noise samples.
CRIS-Team Scopus Importer:2021-04-19
Keywords: Corcos model; multi-channel; Noise reduction; spatial coherence; wind noise

Wind speed and direction estimation based on the spatial coherence of closely spaced microphones - Mirabilii, Daniele; Habets, Emanuël (Journal of the Acoustical Society of America, 2022)
Wind-induced noise recorded with a compact microphone array can be exploited to infer the mean velocity of the free-field airflow. In this work, a model-based method to estimate the wind flow speed and direction is proposed that uses spectro-spatial correlations of closely spaced microphone signals. As shown in a recent work by the present authors, the normalized cross-power spectral density of flow-induced noise measured with closely spaced microphones, also referred to as the spatial coherence, can be approximated by a semi-empirical model, named the Corcos model. Due to the dependency of the Corcos model on the airflow velocity, the measured spatial coherence provides information on the sought quantity. Speed and direction can be resolved by fitting the measured spatial coherence to the analytical Corcos model in the least squares sense. The accuracy of the proposed method is investigated across a range of wind speed between 0.5 and 12 ms-1 and all directions, using observation lengths from 5 s to 1 h. The audio samples under test were recorded indoors and outdoors and labeled by an ultrasonic anemometer. The evaluation results show that the accuracy can be increased by reducing the time resolution.
CRIS-Team Scopus Importer:2022-02-11