Paper Archive

This article discusses the implementation of frequency domain digital audio effects using the Csound 5 music programming language, with its streaming frequency-domain signal (fsig) framework. Introduced to Csound 4.13, by Richard Dobson, it was further extended by Victor Lazzarini in version 5. The latest release of Csound incorporates a variety of new opcodes for different types of spectral manipulations. This article introduces the fsig framework and the analysis and resynthesis unit generators. It describes in detail the different types of spectral DAFx made possible by these new opcodes.

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The concatenative real-time sound synthesis system CataRT plays grains from a large corpus of segmented and descriptor-analysed sounds according to proximity to a target position in the descriptor space. This can be seen as a content-based extension to granular synthesis providing direct access to specific sound characteristics. CataRT is implemented in Max/MSP using the FTM library and an SQL database. Segmentation and MPEG-7 descriptors are loaded from SDIF files or generated on-the-fly. CataRT allows to explore the corpus interactively or via a target sequencer, to resynthesise an audio file or live input with the source sounds, or to experiment with expressive speech synthesis and gestural control.

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This paper gives an overview of one of the most important features in our synthesis language called PWGLSynth. We will concentrate on how to represent visually multichannel signals in a synthesis patch. PWGLSynth synthesis boxes support vectored inputs and outputs. This scheme is useful as it allows to construct compound entities which are used often in sound synthesis such as banks, parallel structures, serial structures, etc. PWGLSynth provides a rich set of tools that allow to manipulate vectors. For instance vectors can mixed, modulated, merged, or split into sub-vectors.

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In this paper we show the possibility of using FAUST (a programming language for function based block oriented programming) to create a fast audio processor in a single chip FPGA environment. The produced VHDL code is embedded in the on-chip processor system and utilizes the FPGA fabric for parallel processing. For the purpose of implementing and testing the code a complete System-On-Chip framework has been created. We use a Digilent board with a XILINX Virtex 2 Pro FPGA. The chip has a PowerPC 405 core and the framework uses the on chip peripheral bus to interface the core. The content of this paper presents a proof-of-concept implementation using a simple two pole IIR filter. The produced code is working, although more work has to be done for implementing complex arithmetic operations support.

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Low bit rate parametric coding of multichannel audio is mainly based on Binaural Cue Coding (BCC). Another multichannel audio processing method called upmix can also be used to deliver multichannel audio, typically 5.1 signals, at low data rates. More precisely, we focus on existing upmix method based on Principal Component Analysis (PCA). This PCA-based upmix method aims at blindly create a realistic multichannel output signal while BCC scheme aims at perceptually restitute the original multichannel audio signal. PCA-based upmix method and BCC scheme both use spatial parameters extracted from stereo channels to generate auditory events with correct spatial attributes i.e. sound sources positions and spatial impression. In this paper, we expose a multichannel audio model based on PCA which allows a parametric representation of multichannel audio. Considering stereo audio, signals resulting from PCA can be represented as a principal component, corresponding to directional sources, and one remaining signal, corresponding to ambience signals, which are both related to original input with PCA transformation parameters. We apply the analysis results to propose a new parametric coding method of stereo audio based on subband PCA processing. The quantization of spatial and energetic parameters is presented and then associated with a state-of-the-art monophonic coder in order to derive subjective listening test results.

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An algebraic technique which computes nonlinear, delay-free digital filter networks is applied to model the Dolby B in the discretetime. The model preserves the topology of the analog system, and imports the characteristics of the nonlinear processing blocks which are responsible of the peculiar functioning of Dolby B. The resulting numerical system exhibits qualitatively similar dynamic behavior and performance – full compliance with the Dolby B specifications would be achieved by deriving, from comprehensive data sheets of the system, accurate discrete-time models of the analog processing blocks. Results demonstrate that the computation converges if proper iterative methods are employed.

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In this paper we propose an iterative audio restoration algorithm based on an autoregressive (AR) model with modeling of the noise pulse template to detect and restore Cell-phone electromagnetic interference (EMI) patterns known as “GSM buzz”. The algorithm is purely software based and does not require the aid of any hardware providing side information. The only assumption is that individual pulses are similar to scaled versions of the known template. With this assumption, the algorithm can fully detect and restore noisy interference signals in real time with almost no audible artifacts and improve the signal to noise ratio by as much as 50dB.

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Raster scanning is a technique for generating or recording a video image by means of a line-by-line sweep, tantamount to a data mapping scheme between one and two dimensional spaces. While this geometric structure has been widely used on many data transmission and storage systems as well as most video displaying and capturing devices, its application to audio related research or art is rare. In this paper, a data mapping mechanism of raster scanning is proposed as a framework for both image sonification and sound visualization. This mechanism is simple, and produces compelling results when used for sonifying image texture and visualizing sound timbre. In addition to its potential as a cross modal representation, its complementary and analogous property can be applied sequentially to create a chain of sonifications and visualizations using digital filters, thus suggesting a useful creative method of audio processing. Special attention is paid to the rastrogram - raster visualization of sound - as an intuitive visual interface to audio data. In addition to being an efficient means of sound representation that provides meaningful display of significant auditory features, the rastrogram is applied to the area of sound analysis by visualizing characteristics of loop filters used for a Karplus-Strong model. A new sound synthesis method based on texture analysis/synthesis of the rastrogram is also suggested.

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This article discusses some developments relating to environments for Csound programming, composition and performance. It introduces the Csound 5 API and discusses its use in the development of a TclTk scripting interface, TclCsound. The three components of TclCsound are presented and discussed. A number of applications, from simple transport control of Csound to client-server networking are explained in some detail. The new multi-platform version of CECILIA is presented. Cecilia is the first Csound frontend to use the functionalities of TclCsound.

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A sound scene can be defined as any “environmental” sound that has a consistent background texture, with one or more potentially recurring foreground events. We describe a data-driven framework for analyzing, transforming, and synthesizing high-quality sound scenes, with flexible control over the components of the synthesized sound. Given one or more sound scenes, we provide well-defined means to: (1) identify points of interest in the sound and extract them into reusable templates, (2) transform sound components independently of the background or other events, (3) continually re-synthesize the background texture in a perceptually convincing manner, and (4) controllably place event templates over the background, varying key parameters such as density, periodicity, relative loudness, and spatial positioning. Contributions include: techniques and paradigms for template selection and extraction, independent sound transformation and flexible re-synthesis; extensions to a wavelet-based background analysis/synthesis; and user interfaces to facilitate the various phases. Given this framework, it is possible to completely transform an existing sound scene, dynamically generate sound scenes of unlimited length, and construct new sound scenes by combining elements from different sound scenes. URL: http://taps.cs.princeton.edu/

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