Paper Archive

In this paper, we propose the use of real-valued Linear Recurrent Units (LRUs) for black-box modeling of audio circuits. A network architecture composed of real LRU blocks interleaved with nonlinear processing stages is proposed. Two case studies are presented, a second-order diode clipper and an overdrive distortion pedal. Furthermore, we show how to integrate the antiderivative antialiaisng technique into the proposed method, effectively lowering oversampling requirements. Our experiments show that the proposed method generates models that accurately capture the nonlinear dynamics of the examined devices and are highly efficient, which makes them suitable for real-time operation inside Digital Audio Workstations.

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Many existing monaural source separation systems use sinusoidal modeling to represent pitched musical sounds during the separation process. In these sinusoidal modeling systems, a musical sound is represented by a sum of time-varying sinusoidal components, and the goal of source separation is to estimate the parameters of each component. Here, we propose an instrument-specific sinusoidal model tailored for a piano tone. Based on our proposed Piano Model, we develop a monaural source separation system to extract each individual tone from mixture signals of piano tones and at the same time, to identify the intensity and adjust the onset of each tone for characterizing the nuance of the music performance. The major difficulty of the source separation problem is to resolve overlapping partials. Our solution collects the training data from isolated tones to train our Piano Model which can capture the common properties across the reappearance of pitches that helps to separate the mixtures. This approach enables high separation quality even for the case of octaves in which the partials of the upper tone completely overlap with those of the lower tone. The results show that our proposed system gives robust and accurate separation of piano tone signal mixtures (including octaves), with the quality significantly better than those reported in the previous work.

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In the sinusoidal analysis of sound, using the Short Time Fourier Transform (STFT), there is the assumption that the signal is locally stationary within each FFT frame. If, as in practice, this assumption is violated, the spectrum becomes distorted. Phase Distortion Analysis (PDA) was introduced in 1995 [1] to enhance the analysis of degraded peaks, by using the distortion itself as a source of information about the signal nonstationarity. It was shown that the first order frequency and amplitude modulation could be measured from the degree of phase shift close to the maximum of the mainlobe peak. This paper presents advances with the PDA technique, in particular a neural network implementation that makes estimation robust to noise. The capability to analyse nonstationarities relaxes the restraint on keeping the FFT analysis window short and therefore effectively improves time-frequency resolution. This, in turn, promises greater analysis-synthesis quality through improved identification and tracking of partials during the analysis phase.

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Virtual reality applications require all kinds of methods to create plausible virtual acoustics environments to enhance the user experience. Here, we present an acoustic paintbrush method that modifies the timbre of a simple room acoustics simulation with the timbre of a measured room response while aiming to preserve the spatial aspects of the simulated room. In other words, the method only applies the measured spectral coloration and alters the simulated and temporal distribution of early reflections as little as possible. Three variations of the acoustic paintbrush method are validated with a listening test. The results indicate that the method works reasonably well. The paintbrushed room acoustic simulations were perceived to become closer to the measured room acoustics than the source simulation. However, the limits of the perceived effect varied depending on the input signal and the simulated and recorded responses. This warrants for further perceptual testing.

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In many applications of audio signal processing modeling of the signal is required. The most commonly used approach for audio signal modeling is to assume the audio signal as an (autoregressive) AR-process where the audio signal is locally stationary over a relatively short time interval. In this case the audio signal can be modeled with an all-pole IIR (infinite impulse response) filter, which leads to LPC (linear predictive coding) where the current input sample is predicted by a linear combination of past samples of the input signal. However, in practice the relatively short time interval (i.e. a frame) where the signal is stationary will vary significantly in the audio signal data stream. Also the information content of the frames will show considerable variation. For a proper modeling of an audio signal it is essential that a suitable frame size and appropriate number of model parameters is used instead of a constant frame size and model order. In this paper we present an adaptive frame-by-frame technique for modeling audio signals, which automatically adjusts the optimal modeling frame size and the optimal number of model parameters for each frame.

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Active acoustic enhancement systems (AAESs) alter the perceived acoustics of a space by using microphones and loudspeakers to introduce sound energy into the room. Double-sloped energy decay may be observed in these systems. However, it is unclear as to which conditions lead to this effect, and to what extent double sloping reduces the perceived naturalness of the reverberation compared to Sabine decay. This paper uses simulated combinations of AAES parameters to identify which cases affect the objective curvature of the energy decay. A subjective test with trained listeners assessed the naturalness of these conditions. Using an AAES model, room impulse responses were generated for varying room dimensions, absorption coefficients, channel counts, system loop gains and reverberation times (RTs) of the artificial reverberator. The objective double sloping was strongly correlated to the ratio between the reverberator and passive room RTs, but parameters such as absorption and room size did not have a profound effect on curvature. It was found that double sloping significantly reduced the perceived naturalness of the reverberation, especially when the reverberator RT was greater than two times that of the passive room. Double sloping had more effect on the naturalness ratings when subjects listened to a more absorptive passive room, and also when using speech rather than transient stimuli. Lowering the loop gain by 9 dB increased the naturalness of the doublesloped stimuli, where some were rated as significantly more natural than the Sabine decay stimuli from the passive room.

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In this paper we present a working system for separating a piano recording into events representing individual piano notes. Each note is parameterized with a transient-plus-harmonics model that, should all the parameters be reliably estimated, would produce near perfect reconstruction for each note as well as for the whole recording. However, interference between overlapping notes makes it hard to estimate parameters from their combination. In this work we propose to assess the estimability of sinusoidal parameters via their apparent degree of interference, estimate the estimable ones using algorithms suitable for different interference situations, and infer the hard-to-estimate parameters from the estimated ones. The outcome is a sequence of separate, parameterized piano notes that perceptually highly resemble, if are not identical to, the notes in the original recording. This allows for later analysis and processing stages using algorithms designed for separate notes.

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The separation of musical instruments acoustically mixed in one source is a very active field which has been approached from many different viewpoints. This article compares the blind source separation perspective and oscillatory correlation theory taking the auditory scene analysis as the point of departure (ASA). The former technique deals with the separation of a particular signal from a mixture with many others from a statistical point of view. Through the standard Independent Component Analysis (ICA), a blind source separation can be done using the particular and the mixed signals' statistical properties. Thus, the technique is general and does not use previous knowledge about musical instruments. In the second approach, an ASA extension is studied with a dynamic neural model which is able to separate the different musical instruments taking a priori unknown perceptual elements as a point of departure. Applying an inverse transformation to the output of the model, the different contributions to the mixture can be recovered again in the time domain.

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MultiBin is a new tool for binaural audition of multiple sound sources in a user definable environment. Although designed to be flexible in its application, its primary function is to provide dynamic multi-channel binaural simulation. It is built upon two new Csound binaural reverberation opcodes. An early reflection opcode, based on an image source method and a Head-Related Transfer Function interpolation algorithm previously introduced by the authors provides dynamic source and listener location. This is complemented by a later reverberation opcode which provides a diffuse reverb based on a parametric Feedback Delay Network model which considers interaural coherence.

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In this paper we propose a digital simulation of an analog amplifier circuit based on a grounded-cathode amplifier with parametric tube model. The time-domain solution enables the online valve model substitution and zero-latency changes in polarization parameters. The implementation also allows the user to match various types of tube processing features.

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