LOW-DIMENSIONAL AUDIO-RATE CONTROL OF FFT-BASED PROCESSING

For some years, real-time general-purpose digital audio systems, based around specialized hardware, have been used by composers and researchers in the field of electronic music, and by professionals in various audio-related fields. During the past decade, these systems have gradually replaced many of the audio-processing devices used in amateur and professional configurations. Today, with the significant increase in computing power available on the desktop, the audio community is witnessing an important shift away from these systems, which required specialized hardware, towards general purpose desktop computing systems featuring high-level digital signal processing (DSP) software programming environments. Introduction An new real-time DSP programming environment called Max Signal Processing (MSP) [Zicarelli, 1997], was released this past year for the Apple Macintosh PowerPC platform. This software offers a suite of signal processing objects as an extension to the widely used MAX software environment, and provides new opportunities for musicians and engineers wishing to explore professional-quality real-time DSP. Most important, MSP provides a number of frequency-domain processing primitives that allow for the development of sophisticated frequency-domain signal processing applications.

ArXiv, 2021

Since the evolution of digital computers, the storage of data has always been in terms of discrete bits that can store values of either 1 or 0. Hence, all computer programs (such as MATLAB), convert any input continuous signal into a discrete dataset. Applying this to oscillating signals, such as audio, opens a domain for processing as well as editing. The Fourier transform, which is an integral over infinite limits, for the use of signal processing is discrete. The essential feature of the Fourier transform is to decompose any signal into a combination of multiple sinusoidal waves that are easy to deal with. The discrete Fourier transform (DFT) can be represented as a matrix, with each data point acting as an orthogonal point, allowing one to perform complicated transformations on individual frequencies. Due to this formulation, all the concepts of linear algebra and linear transforms prove to be extremely useful here. In this paper, we first explain the theoretical basis of audio ...

Proceedings of the 31st International Conference …, 2003

2003

From the very beginning of computer music, the Fast Fourier Transform (FFT) has been considered a powerful technique that allowed the development of many valuable tools for research and transformation of digital sound. The reader may consult -among othersthe works by ( Moore,1990, 1978, Embree & Kimble, 1991, Moorer, 1978, and Wessel & Risset, 1985), in order to obtain the basic concepts needed for a detailed comprehension of the following discussion.

University of Salford, UK, 2003

The phase vocoder is an analysis-synthesis system linked to a) transmission of speech and music signals through telephone lines and b) radio communication techniques such as heterodyning. The phase vocoder program ‘pvoc’ has been implemented at the Computer and Audio Research Laboratory (CARL) for musical purposes in the 1980s and adapted more recently to the personal computer (PC) by the Composers’ Desktop Project (CDP) using ‘pvocex’. The most recent CDP version of the CARL phase vocoder is ‘pvocex2’, which has been further developed in this final-year project to manipulate the short-time Fourier transforms (STFTs) produced by the program at the analysis stage.

2011

This paper describes the implementation of an innovative musical interface based on the sound localization capability of a microphone array. Our proposal is to allow a musician to plan and conduct the expressivity of a performance, by controlling in realtime an audio processing module through the spatial movement of a sound source, i.e. voice, traditional musical instruments, sounding mobile devices. The proposed interface is able to locate and track the sound in a two-dimensional space with accuracy, so that the x-y coordinates of the sound source can be used to control the processing parameters. In particular, the paper is focused on the localization and tracking of harmonic sound sources in real moderate reverberant and noisy environment. To this purpose, we designed a system based on adaptive parameterized Generalized Cross-Correlation (GCC) and Phase Transform (PHAT) weighting with Zero-Crossing Rate (ZCR) threshold, a Wiener filter to improve the Signal to Noise Ratio (SNR) and a Kalman filter to make the position estimation more robust and accurate. We developed a Max/MSP external objects to test the system in a real scenario and to validate its usability.

2014 International Conference on Signal Processing and Integrated Networks (SPIN), 2014

In recent years, the DSP group at the University of Manchester has developed a range of DSP platforms for realtime filtering and processing of acoustic signals. These include Signal Wizard 2.5, Signal Wizard 3 and Vsound. These incorporate processors operating at 100 million multiplicationaccumulations per second (MMACs) for SW 2.5 and 600 MMACS for SW 3 and Vsound. SW 3 features six input and eight output analogue channels, digital input/output in the form of S/PDIF and a USB interface. For all devices, The software allows the user, with no knowledge of filter theory or programming, to design and run standard or completely arbitrary FIR, IIR and adaptive filters. Processing tasks are specified using the graphical icon based interface. In addition, the system has the capability to emulate in real-time linear system behavior such as sensors, instrument bodies, string vibrations, resonant spaces and electrical networks. Tests have confirmed a high degree of fidelity between the behavior of the physical system and its digitally emulated counterpart. In addition to the supplied software, the user may also program the system using a variety of commercial packages via the JTAG interface.

2000

No matter how,complex DSP algorithms are a nd ho w,rich sonic processes they produce, the issue of their control immediately arises when they are used by musicians, independently on their knowledge of the underlying mathematics or their degree ,of familiarity ,with ,the ,design of digital instruments. This text will analyze the problem of the c ontrol of DSP ,modules

Applying the lifting scheme for the construction of wavelets filter bank allows significantly reduce the number of arithmetic operations that are necessary to compute the transform. A folded parallel architecture for lifting-based DWPT was presented in . It consists of a group of MACs operating in parallel on the data prestored in a memory bank. In [10] is proposed an architecture using a direct implementation of a lifting-based wavelet filter to perform one level of DWPT at a time. The main drawback of these existing architectures is that they all use memory to store the intermediate coefficients and involve intense memory access during the computation. A recursive pyramid algorithm based folded architecture for computing liftingbased multilevel DWPT is presented in . However, the scheduling and control complexity is high, which also introduce large numbers of switches, multiplexers and control signals. The architecture is not regular and need to be modified for different number of level of DWPT computation. A folded architecture for lifting-based wavelet filters is proposed in [12] to compute the wavelet butterflies in different groups simultaneously at each decomposition level. According to the comparison results, the proposed architecture is more efficient than the previous proposed architectures in terms of memory access, hardware regularity and simplicity, and throughput. It is necessary to notice, that the architecture of the given processor is effective only for calculation of full tree DWPT. Here there is no technique of management for DWPT with best tree searching.