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The knowledge of frequency-dependent spatiotemporal features of the reflected soundfield is essential in optimizing the perception quality of spatial audio applications. For this purpose, we need a reliable room acoustic analyzer that can conceive the spatial variations in a decaying reflected soundfield according to the frequency-dependent surface properties and source directivity. This paper introduces a time-frequency-dependent angular reflection power distribution model represented by a von Mises–Fisher (vMF) mixture function to facilitate manifold analysis of a reverberant soundfield. The proposed approach utilizes the spatial correlation of higher-order eigenbeams to deduce the directional reflection power vectors, which are then synthesized into a vMF mixture model. The experimental study demonstrates the directional power variations of early reflections and late reverberations across different frequencies. This work also introduces a measure called the directivity time-span to quantify the duration of anisotropic reflections before it decays into a totally diffused field. We validate the subband performance by comparing it with the eigenbeam multiple signal classification method. The results prove the influence of source position, source directivity, and room environment in the distribution of reflection power, whereas the directivity time-span behaves independent of the source positions.

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 (SDNR). We propose a solution (i) by using a 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. The theory developed is applicable to irregular microphone arrays embedded on a drone enabling realistic integration to most drones. We demonstrate the validity of the proposed framework through (i) experimental recordings from two different drone acoustics datasets and (ii) outdoor measurements from a hovering drone for a bioacoustic application. The results confirm improved performance in terms of SDNR, speech quality (PESQ), and intelligibility (STOI) at very low SDNR levels (up to -30 dB) and show a strong potential for signal enhancement applications using noisy drones.

Sound field reproduction algorithms require loudspeaker directivity, which is usually measured at discrete frequencies. A time domain model of loudspeaker directivity benefits broadband applications. This Letter proposes the concept of a directional wave front in the time domain, which could be linked to loudspeaker impulse responses measured on a spherical surface. The observed signal in the time domain at the boundary of a spherical region due to a propagating directional wave front is illustrated using a geometric model. Based on the geometric model, the spherical harmonic decomposition of the observed signal in the time domain is also derived.

In this paper, we consider the problem of recovering speech from audio recordings of on-board microphones in a noisy drone platform. Enhancement of speech 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 (SdNR). We propose a solution by (i) developing a 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 the known statistics of motor current-specific drone noise. The theory developed is applicable to irregular microphone array embedded in a drone enabling realistic integration to most drones. We evaluate the proposed framework using two different drone acoustics datasets under extreme SdNR levels including −30 dB. The experimental results confirm promising performance in terms of SdNR improvement, speech quality (PESQ), and intelligibility (STOI) and show a strong potential for speech enhancement applications using noisy drones.

Active noise control (ANC) over an extended spatial region using multiple microphones and multiple loudspeakers has become an important problem. The maximum noise reduction (NR) potential over the control area is a critical evaluation variable as it indicates the fundamental limitation of a given ANC system. In this paper, a method to mathematically formulate the NR potential for any given multichannel ANC systems is developed. First, the residual error in the multichannel feedforward ANC system is formulated, and then the multiple-input-multiple-output problem is decomposed into the parallel-channel problem. The total energy of the residual error is further decomposed into three different terms representing (i) the signal coherence between the reference signals and error signals, (ii) the filter, and (iii) the system null space. The experimental results validate the proposed evaluation method and illustrate the effectiveness on the maximum NR performance evaluation for given systems. Using the proposed analyzing method, more insight into the contribution of each component to the NR potential can be achieved.