Download Binaural Dark-Velvet-Noise Reverberator Binaural late-reverberation modeling necessitates the synthesis of frequency-dependent inter-aural coherence, a crucial aspect of spatial auditory perception. Prior studies have explored methodologies such as filtering and cross-mixing two incoherent late reverberation impulse responses to emulate the coherence observed in measured binaural late reverberation. In this study, we introduce two variants of the binaural dark-velvet-noise reverberator. The first one uses cross-mixing of two incoherent dark-velvet-noise sequences that can be generated efficiently. The second variant is a novel time-domain jitter-based approach. The methods’ accuracies are assessed through objective and subjective evaluations, revealing that both methods yield comparable performance and clear improvements over using incoherent sequences. Moreover, the advantages of the jitter-based approach over cross-mixing are highlighted by introducing a parametric width control, based on the jitter-distribution width, into the binaural dark velvet noise reverberator. The jitter-based approach can also introduce timedependent coherence modifications without additional computational cost.
Download One-to-Many Conversion for Percussive Samples A filtering algorithm for generating subtle random variations in
sampled sounds is proposed. Using only one recording for impact
sound effects or drum machine sounds results in unrealistic repetitiveness during consecutive playback. This paper studies spectral
variations in repeated knocking sounds and in three drum sounds:
a hihat, a snare, and a tomtom. The proposed method uses a short
pseudo-random velvet-noise filter and a low-shelf filter to produce
timbral variations targeted at appropriate spectral regions, yielding potentially an endless number of new realistic versions of a
single percussive sampled sound.
The realism of the resulting
processed sounds is studied in a listening test. The results show
that the sound quality obtained with the proposed algorithm is at
least as good as that of a previous method while using 77% fewer
computational operations. The algorithm is widely applicable to
computer-generated music and game audio.