Image method for efficiently simulating small‐room acoustics

By J. Allen, D. Berkley. 1976

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In Signal Processing

Citations in the corpus

Multi-Task Self-Supervised Learning for Robust Speech Recognition (2020, positive): “Reverberation is introduced by convolving the input signal with a set of 1300 impulse responses derived with the image method [18].”
Light Gated Recurrent Units for Speech Recognition (2018, methodology): “are based on the image method [55] and correspond to four different reverberation times T60 (ranging from 250 to 1000 ms).”
Time-Frequency Masking in the Complex Domain for Speech Dereverberation and Denoising (2017, positive): “Simulated RIRs are generated using the imaging method [1], which is implemented in [12].”
Binaural Classification for Reverberant Speech Segregation Using Deep Neural Networks (2014, methodology): “The reflection paths of a particular sound source are obtained using the image model for a small rectangular room [1].”
SpeakerBeam: Speaker Aware Neural Network for Target Speaker Extraction in Speech Mixtures (2019, methodology): “It is created by convolving the data with room impulse responses generated with the image method [49], [50] to simulate an 8-channel microphone array.”
Machine learning in acoustics: Theory and applications. (2019, background): “Until recently, a main paradigm in localization research was based on certain statistical and physical assumptions regarding the propagation of sound sources, 149,150 and mainly focused on robust methods to extract the direct-path.”
ICASSP 2023 Deep Noise Suppression Challenge (2023, methodology): “Impulse responses used for training this model consists of 150,000 simulated impulse responses at sampling rate 48kHz using the Image Source method [17].”
Acoustic Modeling for Overlapping Speech Recognition: Jhu Chime-5 Challenge System (2019, methodology): “In the ﬁrst method, we use the image method proposed by Allen and Berkeley in [14] to generate the RIR for each recording.”
On Optimal Frequency-Domain Multichannel Linear Filtering for Noise Reduction (2010, background): “It is also important to note that thanks to the new simplified expression of the signal distortion index (37), one can find the relationship between defined in the optimization problem (12) and the tuning parameter .”
Pyroomacoustics: A Python Package for Audio Room Simulation and Array Processing Algorithms (2017, methodology): “In room acoustics, simulation based on the image source model (ISM) has been used extensively for this purpose and has wellknown strengths and weaknesses [5].”
A two-stage algorithm for one-microphone reverberant speech enhancement (2006, methodology): “Berkley and Allen [8] identified that two physical variables, reverberation time and the talker–listener distance, are important for reverberant speech quality.”
A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation (2017, unknown): “A variant of this approach which enables even more precise control of the setup is to use artiﬁcial AIRs [288], [289] simulated using, e.g., Roomsim [290] 16 or Room generator 17 and the respective spherical harmonic domain variant SMIRgen [291] 18 .”