Adaptive digital audio effects (a-DAFx): a new class of sound transformations

Verfaille, Vincent; Zölzer, Udo; Arfib, Daniel

doi:10.1109/tsa.2005.858531

Cited by 43 publications

(31 citation statements)

References 37 publications

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“…Whilst the axes of the two-dimensional space are somewhat arbitrary, underlying timbral characteristics are projected onto the space via a training stage using two-term musical semantics data. In addition to this, we propose a signal processing method of adapting the parameter modulation process to the incoming audio data based on feature extraction applied to the long-term average spectrum (LTAS), as detailed in [17][18][19], capable of running in near-real-time. The model is implemented using the SAFE architecture (detailed in [20]), and is provided as an extension of the current Semantic Audio Parametric Equaliser (available for download at [21]), as shown in Figure 1a.…”

Section: Aimsmentioning

confidence: 99%

Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space

2016

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Abstract:Equalisation is one of the most commonly-used tools in sound production, allowing users to control the gains of different frequency components in an audio signal. In this paper we present a model for mapping a set of equalisation parameters to a reduced dimensionality space. The purpose of this approach is to allow a user to interact with the system in an intuitive way through both the reduction of the number of parameters and the elimination of technical knowledge required to creatively equalise the input audio. The proposed model represents 13 equaliser parameters on a two-dimensional plane, which is trained with data extracted from a semantic equalisation plug-in, using the timbral adjectives warm and bright. We also include a parameter weighting stage in order to scale the input parameters to spectral features of the audio signal, making the system adaptive. To maximise the efficacy of the model, we evaluate a variety of dimensionality reduction and regression techniques, assessing the performance of both parameter reconstruction and structural preservation in low-dimensional space. After selecting an appropriate model based on the evaluation criteria, we conclude by subjectively evaluating the system using listening tests.

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Section: Aimsmentioning

confidence: 99%

Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space

2016

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“…Rather than have sound engineers manually apply many audio effects to all audio inputs and determine their appropriate parameter settings, intelligent, adaptive digital audio effects may be applied instead [8]. The parameter settings of adaptive effects are determined by analysis of the audio content, where the analysis is achieved by a feature extraction component built into the effect.…”

Section: Intelligent and Adaptive Digital Audio Effectsmentioning

confidence: 99%

Intelligent systems for mixing multichannel audio

Reiss

2011

2011 17th International Conference on Digital Signal Processing (DSP)

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Multichannel signal processing techniques are usually concerned with extracting information about sources from several received signals. In this paper, we describe an emerging field of multichannel audio signal processing where the inter-channel relationships are exploited in order to manipulate the multichannel content. Applications to realtime, automatic audio production are described and the necessary technologies and the architecture of such systems are presented. The current state of the art is reviewed, and directions of future research are also discussed.

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“…However, in our case we are specifically concerned with the application of audio effects in multi-track music production, where mostly mono-timbral signals are modified for either creative purposes or to enhance the quality of a given recording. An overview of audio effects and their classification is given in [8]. Figure 2 shows a simplified block diagram of the demonstration system.…”

Section: Audio Dsp Effectsmentioning

confidence: 99%

RDFx: Audio Effects Utilising Musical Metadata

Wilmering

Sandler

2010

2010 IEEE Fourth International Conference on Semantic Computing

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We present a system of audio effects capable of processing and predicting metadata associated with the processed signals. This new class of audio effects has additional inputs and outputs describing features in RDF using the Audio Features Ontology. This data is processed in parallel to the audio signals. We show that by deriving the changes in metadata resulting from audio processing directly from the effect parameters, we can increase the accuracy of descriptors and reduce the computational cost by omitting repeated application of feature extraction algorithms on the output signal. Our demonstration uses a modified version of Sonic Visualiser controlled via the Open Sound Control protocol (OSC) to display metadata at various stages of processing.

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Adaptive digital audio effects (a-DAFx): a new class of sound transformations

Cited by 43 publications

References 37 publications

Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space

Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space

Intelligent systems for mixing multichannel audio

RDFx: Audio Effects Utilising Musical Metadata

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