Download Flexible Real-Time Reverberation Synthesis With Accurate Parameter Control Reverberation is one of the most important effects used in audio
production. Although nowadays numerous real-time implementations of artificial reverberation algorithms are available, many of
them depend on a database of recorded or pre-synthesized room
impulse responses, which are convolved with the input signal. Implementations that use an algorithmic approach are more flexible
but do not let the users have full control over the produced sound,
allowing only a few selected parameters to be altered. The realtime implementation of an artificial reverberation synthesizer presented in this study introduces an audio plugin based on a feedback delay network (FDN), which lets the user have full and detailed insight into the produced reverb. It allows for control of
reverberation time in ten octave bands, simultaneously allowing
adjusting the feedback matrix type and delay-line lengths. The
proposed plugin explores various FDN setups, showing that the
lowest useful order for high-quality sound is 16, and that in the
case of a Householder matrix the implementation strongly affects
the resulting reverberation. Experimenting with delay lengths and
distribution demonstrates that choosing too wide or too narrow a
length range is disadvantageous to the synthesized sound quality.
The study also discusses CPU usage for different FDN orders and
plugin states.
Download Virtual Bass System With Fuzzy Separation of Tones and Transients A virtual bass system creates an impression of bass perception
in sound systems with weak low-frequency reproduction, which
is typical of small loudspeakers. Virtual bass systems extend the
bandwidth of the low-frequency audio content using either a nonlinear function or a phase vocoder, and add the processed signal
to the reproduced sound. Hybrid systems separate transients and
steady-state sounds, which are processed separately. It is still challenging to reach a good sound quality using a virtual bass system.
This paper proposes a novel method, which separates the tonal,
transient, and noisy parts of the audio signal in a fuzzy way, and
then processes only the transients and tones. Those upper harmonics, which can be detected above the cutoff frequency, are boosted
using timbre-matched weights, but missing upper harmonics are
generated to assist the missing fundamental phenomenon. Listening test results show that the proposed algorithm outperforms selected previous methods in terms of perceived bass sound quality.
The proposed method can enhance the bass sound perception of
small loudspeakers, such as those used in laptop computers and
mobile devices.
Download Velvet-Noise Feedback Delay Network Artificial reverberation is an audio effect used to simulate the acoustics of a space while controlling its aesthetics, particularly on sounds
recorded in a dry studio environment. Delay-based methods are
a family of artificial reverberators using recirculating delay lines
to create this effect.
The feedback delay network is a popular
delay-based reverberator providing a comprehensive framework
for parametric reverberation by formalizing the recirculation of
a set of interconnected delay lines. However, one known limitation of this algorithm is the initial slow build-up of echoes, which
can sound unrealistic, and overcoming this problem often requires
adding more delay lines to the network. In this paper, we study the
effect of adding velvet-noise filters, which have random sparse coefficients, at the input and output branches of the reverberator. The
goal is to increase the echo density while minimizing the spectral coloration. We compare different variations of velvet-noise
filtering and show their benefits. We demonstrate that with velvet
noise, the echo density of a conventional feedback delay network
can be exceeded using half the number of delay lines and saving
over 50% of computing operations in a practical configuration using low-order attenuation filters.
Download Neural Modelling of Time-Varying Effects This paper proposes a grey-box neural network based approach
to modelling LFO modulated time-varying effects.
The neural
network model receives both the unprocessed audio, as well as
the LFO signal, as input. This allows complete control over the
model’s LFO frequency and shape. The neural networks are trained
using guitar audio, which has to be processed by the target effect
and also annotated with the predicted LFO signal before training.
A measurement signal based on regularly spaced chirps was used
to accurately predict the LFO signal. The model architecture has
been previously shown to be capable of running in real-time on a
modern desktop computer, whilst using relatively little processing
power. We validate our approach creating models of both a phaser
and a flanger effects pedal, and theoretically it can be applied to
any LFO modulated time-varying effect. In the best case, an errorto-signal ratio of 1.3% is achieved when modelling a flanger pedal,
and previous work has shown that this corresponds to the model
being nearly indistinguishable from the target device.