Detecting Arrivals in Room Impulse Responses With Dynamic Time Warping

Kelly, Ian J.; Boland, Frank

doi:10.1109/taslp.2014.2321472

Cited by 17 publications

(10 citation statements)

References 19 publications

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“…The process was then run again to detect the location of the reflected component, and each segment was checked to ensure only audio pertaining to the reflected component was present (see Figure 5 for an example BRIR with window locations). When dealing with BRIRs measured in less controlled environments, a method for systematically detecting discrete reflections in the BRIR is required, and various methods have been proposed in the literature to detect reflections in impulse responses, including [4,[33][34][35].…”

Section: Testing Methodologymentioning

confidence: 99%

Application of Machine Learning for the Spatial Analysis of Binaural Room Impulse Responses

Lovedee-Turner

Murphy

2018

Applied Sciences

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Abstract:Spatial impulse response analysis techniques are commonly used in the field of acoustics, as they help to characterise the interaction of sound with an enclosed environment. This paper presents a novel approach for spatial analyses of binaural impulse responses, using a binaural model fronted neural network. The proposed method uses binaural cues utilised by the human auditory system, which are mapped by the neural network to the azimuth direction of arrival classes. A cascade-correlation neural network was trained using a multi-conditional training dataset of head-related impulse responses with added noise. The neural network is tested using a set of binaural impulse responses captured using two dummy head microphones in an anechoic chamber, with a reflective boundary positioned to produce a reflection with a known direction of arrival. Results showed that the neural network was generalisable for the direct sound of the binaural room impulse responses for both dummy head microphones. However, it was found to be less accurate at predicting the direction of arrival of the reflections. The work indicates the potential of using such an algorithm for the spatial analysis of binaural impulse responses, while indicating where the method applied needs to be made more robust for more general application.

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Section: Testing Methodologymentioning

confidence: 99%

Application of Machine Learning for the Spatial Analysis of Binaural Room Impulse Responses

Lovedee-Turner

Murphy

2018

Applied Sciences

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“…To obtain a numerical solution we substitute the partial derivatives with central differences given by (15). where ϵ and τ are the spatial and temporal step sizes.…”

Section: Solving the Acoustic Wave Equationmentioning

confidence: 99%

“…The implementation of the FDTD method is based on the thorough work of Kowalcyzk [13]. It has already been referred to by other authors dealing with room acoustics [14,15].…”

Section: Introductionmentioning

confidence: 99%

Efficiency Testing of Active Noise Control by Acoustic Field Modeling

Szarvas¹,

Sujbert²

2015

Period. Polytech. Elec. Eng. Comp. Sci.

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The efficiency of active noise control (ANC) is largely dependent on the arrangement of the microphones and loudspeakers, and the acoustic environment as well. The paper presents a simulation tool, which allows the performance evaluation of ANC systems without the difficulty of physical experiments. The proposed simulation integrates sound field calculation and noise control. Field calculation is based on robust FDTD techniques, while the discrete time controller is realized in a usual way. The simulation is performed in three stages. First the program determines the discrete transfer function models of the acoustic paths between specific points of interest. In the next stage the controller's operation is evaluated without field simulation. Having the steady state parameters of the adaptive controller, the field simulation is rerun for the reverberation time providing the sound pressure map. Sound field calculation is implemented on modern GPGPU devices. A series of ANC experiments were compared to the results of the simulations, and they prove the applicability of our simulation method in a convincing manner.

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“…The second category [21], [22] localizes an active acoustic observer, equipped with a loudspeaker for actively probing the room and microphones for Room Impulse Response (RIR) measurements, and estimates the room dimensions from the TOAs of early reflections. However, TOA estimation from RIRs is ambiguous [23], and emission of controlled sound stimuli for RIR measurements is highly intrusive to people nearby, and therefore unacceptable in many important use-cases. Moreover, to avoid interference with the RIR measurements, the environment cannot contain sound sources other than the measurement stimuli.…”

Section: Introductionmentioning

confidence: 99%

Acoustic SLAM

Evers

Naylor

2018

IEEE/ACM Trans. Audio Speech Lang. Process.

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An algorithm is presented that enables devices equipped with microphones, such as robots, to move within their environment in order to explore, adapt to, and interact with sound sources of interest. Acoustic scene mapping creates a threedimensional (3D) representation of the positional information of sound sources across time and space. In practice, positional source information is only provided by Direction-of-Arrival (DoA) estimates of the source directions; the source-sensor range is typically difficult to obtain. DoA estimates are also adversely affected by reverberation, noise, and interference, leading to errors in source location estimation and consequent false DoA estimates. Moreover, many acoustic sources, such as human talkers, are not continuously active, such that periods of inactivity lead to missing DoA estimates. Withal, the DoA estimates are specified relative to the observer's sensor location and orientation. Accurate positional information about the observer therefore is crucial. This paper proposes Acoustic Simultaneous Localization and Mapping (aSLAM), which uses acoustic signals to simultaneously map the 3D positions of multiple sound sources while passively localizing the observer within the scene map. The performance of aSLAM is analyzed and evaluated using a series of realistic simulations. Results are presented to show the impact of the observer motion and sound source localization accuracy.

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Detecting Arrivals in Room Impulse Responses With Dynamic Time Warping

Cited by 17 publications

References 19 publications

Application of Machine Learning for the Spatial Analysis of Binaural Room Impulse Responses

Application of Machine Learning for the Spatial Analysis of Binaural Room Impulse Responses

Efficiency Testing of Active Noise Control by Acoustic Field Modeling

Acoustic SLAM

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