DSP Audio Effects: Books, Articles, Websites

A curated list of Wikipedia websites, each featuring dedicated pages for individual audio effects.

In this article, we’ve compiled a complete list of all the different audio effects at your disposal. We have only provided a brief definition and explanation for each audio effect to keep things simple. However, if you’d like to learn more about any particular topic, we’ve provided links to our blog articles that explain these audio effects in more detail.

In this article, we would like to revisit the meaning of audio effects and what makes them so important for our sound. You will understand the differences between them, and when does each audio effect play an optimal role in your music production.

A review of specific hardware audio effect types and models.

Audio effects are used in audio mixing in order to achieve virtually any result you want in a specific track or the overall mix. Having a fundamental understanding of the most commonly used audio effects can help you get your audio where you want it faster. In this overview, we’re going to break down the most commonly used audio effects and explain what each does and when you’ll want to use them. It’s audio effects explained simply and effectively.

The offical, free ebook website of the book "The Scientist and Engineer's Guide to Digital Signal Processing" by Steven W. Smith, Ph.D.

Cookbook formulae for audio equalizer biquad filter coefficients" by Robert Bristow-Johnson
Editors: Raymond Toy, Doug Schepers (previously W3C)
Adapted from Audio-EQ-Cookbook.txt, by Robert Bristow-Johnson, with permission.

A wealth of information for learning about the latest advances in DSP.

Today, we’re pleased to introduce the Differentiable Digital Signal Processing (DDSP) library. DDSP lets you combine the interpretable structure of classical DSP elements (such as filters, oscillators, reverberation, etc.) with the expressivity of deep learning.

Neural networks (such as WaveNet or GANSynth) are often black boxes. They can adapt to different datasets but often overfit details of the dataset and are difficult to interpret. Interpretable models (such as musical grammars) use known structure, so they are easier to understand, but have trouble adapting to diverse datasets.

DSP (Digital Signal Processing, without the extra “differentiable” D) is one of the backbones of modern society, integral to telecommunications, transportation, audio, and many medical technologies. You could fill many books with DSP knowledge, but here are some fun introductions to audio signals, oscillators, and filters, if this is new to you.

The key idea is to use simple interpretable DSP elements to create complex realistic signals by precisely controlling their many parameters. For example, a collection of linear filters and sinusoidal oscillators (DSP elements) can create the sound of a realistic violin if the frequencies and responses are tuned in just the right way. However, it is difficult to dynamically control all of these parameters by hand, which is why synthesizers with simple controls often sound unnatural and “synthetic”.

This paper reports on a funded National Science Foundation (ILI) work to establish a multimedia digital signal processing (MMDSP) laboratory that introduces the classical concepts in digital signal processing within the context of processing and interpretation of signals and images using innovative hands on experiments. The MDSP laboratory enhances students' education through multimedia cooperative learning using state-of-the-art audio and video equipment.

Specific features of the application of digital signal processing in noiseproof broadband impedance measuring analyzers are considered. It is shown that the use solely of the procedure of digital signal filtering is inefficient in this case. We propose to form an array of integrated weighted values of the signal instead of the array of its instantaneous values, thus realizing the procedure of preliminary analog filtering of the signal prior to the stage of analog-to-digital conversion.

The study investigates the application of audio signal processing technology in the realm of music synthesis, aiming to explore its potential in creating, manipulating, and enhancing musical sounds. Through a comprehensive analysis, the research examines various signal-processing techniques and algorithms within the context of digital audio workstations (DAWs), MIDI controllers, and signal-processing software. The experimental setup involves data acquisition, preprocessing, and synthesis techniques encompassing additive, subtractive, frequency modulation (FM), and granular synthesis, as well as processing methods including time stretching, pitch shifting, and reverberation. Evaluation methodologies include subjective listening tests and objective metrics such as signal-to-noise ratio (SNR) and Total Harmonic Distortion (THD). The findings of this study contribute to the understanding of how audio signal processing technology can revolutionize music synthesis, offering insights for musicians, engineers, and researchers alike into harnessing its potential for creative expression and sonic innovation.

In single-carrier wavelength-division multiplexing (WDM) systems, the spectral efficiency can be increased by reducing the channel spacing through digital signal processing (DSP). Two major issues with using tight filtering are cross talk between channels and inter-symbol interference (ISI) within a channel. By fulfilling the Nyquist criterion, Nyquist spectral-shaped WDM systems can achieve narrow channel spacings close to the symbol rate with negligible cross talk and ISI. In principle, DSP can generate any signals with arbitrary waveforms and spectrum shapes. However, the complexity of DSP is limited by its cost and power consumption. It is necessary to optimize the DSP to achieve the required performance at a minimum complexity. In this paper, we first introduced the background of digital signal processing for Nyquist spectral shaping in optical fiber WDM systems. Then, we investigated the use of digital finite impulse response (FIR) filters to generate Nyquist-WDM 16-ary quadrature amplitude modulation (16QAM) signals with the raised-cosine (RC) and root-raised-cosine (RRC) shape spectra. The system performance of both the RC and RRC spectra is also examined. Moreover, we explored the various methods to reduce digital-to-analog converter (DAC) sampling speed, such as using super-Gaussian electrical filters (E-filter) and spectral pre-emphasis. We also discussed receiver-side matched filter design for Nyquist-WDM receiver optimization.

Digital audio tampering detection can be used to verify the authenticity of digital audio. However, most current methods use standard electronic network frequency (ENF) databases for visual comparison analysis of ENF continuity of digital audio or perform feature extraction for classification by machine learning methods. ENF databases are usually tricky to obtain, visual methods have weak feature representation, and machine learning methods have more information loss in features, resulting in low detection accuracy. This paper proposes a fusion method of shallow and deep features to fully use ENF information by exploiting the complementary nature of features at different levels to more accurately describe the changes in inconsistency produced by tampering operations to raw digital audio. Firstly, the audio signal is band-pass filtered to obtain the ENF component. Then, the discrete Fourier transform (DFT) and Hilbert transform are performed to obtain the phase and instantaneous frequency of the ENF component. Secondly, the mean value of the sequence variation is used as the shallow feature; the feature matrix obtained by framing and reshaping of the ENF sequence is used as the input of the convolutional neural network; the characteristics of the fitted coefficients are obtained by curve fitting. Then, the local details of ENF are obtained from the feature matrix by the convolutional neural network, and the global information of ENF is obtained by fitting coefficient features through deep neural network (DNN). The depth features of ENF are composed of ENF global information and local information together. The shallow and deep features are fused using an attention mechanism to give greater weights to features useful for classification and suppress invalid features. Finally, the tampered audio is detected by downscaling and fitting with a DNN containing two fully connected layers, and classification is performed using a Softmax layer. The method achieves 97.03% accuracy on three classic databases: Carioca 1, Carioca 2, and New Spanish. In addition, we have achieved an accuracy of 88.31% on the newly constructed database GAUDI-DI. Experimental results show that the proposed method is superior to the state-of-the-art method.

Signal Wizard Systems® is a digital signal processing (DSP) research venture within the School of EEE at the University of Manchester, UK. It specialises in the development and supply of realtime DSP products for audio signal analysis and processing. The unique and underpinning philosophy of these products is their ease of use. The systems require minimal knowledge of DSP theory on the part of the user and none of the mathematics associated with digital filter design. Filters and other algorithms can be designed in seconds, downloaded and executed in real time with just a few mouse clicks. Since 2004 Signal Wizard products have been sold all over the world for applications ranging from noise suppression, adaptive filtering and system modelling to musical instrument research. In particular, their ease of use ensures that they are ideally suited for teaching simple and more advanced concepts in DSP both at undergraduate and postgraduate level. For this purpose, a DSP laboratory teaching package has been developed using the Signal Wizard range of devices, and has proven an invaluable tool for training our student cohort in the practical aspects of DSP engineering design and programming.