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Proc. of the 18th Int. Conference on Digital Audio Effects (DAFx-15), Trondheim, Norway, Nov 30 - Dec 3, 2015 FEATURE DESIGN FOR THE CLASSIFICATION OF AUDIO EFFECT UNITS BY INPUT / OUTPUT MEASUREMENTS Felix Eichas, Marco Fink, Udo Zölzer Department of Signal Processing and Communications, Helmut-Schmidt-Universität Hamburg, Germany [email protected] ABSTRACT the input signal can be determined and recreated with a digital model to achieve the same characteristics [6]. Virtual analog modeling is an important field of digital audio sig- Black-box identification is still an important topic and there nal processing. It allows to recreate the tonal characteristics of are countless contributions to this domain from neural networks to real-world sound sources or to impress the specific sound of a cer- wavelet-transform based models for identification. Sjöberg et al. tain analog device upon a digital signal on a software basis. Auto- published a summary of different nonlinear modeling techniques matic virtual analog modeling using black-box system identifica- [7]. A method, that has been successfully used to model nonlinear tion based on input/output (I/O) measurements is an emerging ap- audio systems, was introduced by Novak et al. [8]. They used a proach, which can be greatly enhanced by specific pre-processing block-oriented Hammerstein model for the identification of a dis- methods suggesting the best-fitting model to be optimized in the tortion guitar effect pedal. actual identification process. In this work, several features based Feature extraction from audio data is an important topic for on specific test signals are presented allowing to categorize instru- music information retrieval (MIR) [9]. Nevertheless, typical MIR ment effect units into classes of effects, like distortion, compres- features like Zero Crossings or Spectral Flux can not be utilized sion, modulations and similar categories. The categorization of to classify the subtle tonal characteristic of specific audio effect analog effect units is especially challenging due to the wide variety devices. of these effects. For each device, I/O measurements are performed and a set of features is calculated to allow the classification. The The majority of commercial digital audio effects, emulating features are computed for several effect units to evaluate their ap- a specific device, are parametric digital models which are usually plicability using a basic classifier based on pattern matching. tweaked by a professional sound engineer to approach the sound of the analog unit. This identification procedure can be automated using black-box modeling. The proposed feature set can facilitate 1. INTRODUCTION the decision which digital model is best-suited to reproduce the characteristics of the analog effects unit under test. For this pur- Audio effect units are used by musicians or sound engineers to pose specifically designed input signals are generated, replayed transform the signal of an (electrical) instrument in order to mod- through the device, and, by recording the output, the influence of ify it in a certain way. Many of these effect units already have been the system on these signals is measured and different characteris- emulated and digital models have been derived to apply these ef- tics are extracted. fects while, for example, mixing a recording [1]. Many musicians In future work, the extracted features can be used to classify value vintage music equipment because of its specific tonal charac- the DUT and choose an appropriate model from a model set. The teristic and want to recreate this characteristic without spending a classification can be done in several ways, e.g. with a neural net- large amount of money on rare vintage equipment. With the aid of work or a weighted decision tree. Once a model is chosen, an iden- system identification, the unique properties of an effects unit can tification algorithm can be used to fit the model’s characteristics to be captured and emulated as a VST plugin or with a DSP-based resemble the DUT. effects unit in a live-setup. This paper describes the signal model for several typical ef- A lot of effect units have been emulated in very different ways. fects in section 2, the input signals designed for the feature com- Modeling via circuit analysis, as done by [2–5], is realized by putation are shown in section 3, and the computation of the fea- transforming the schematic of the device into a digital model. This tures is explained in section 4. In section 5 the measurement-setup procedure is very precise and can describe effect units accurately. is shown and the features are evaluated by measuring some typical But it also has several drawbacks. The computational load is very effect pedals and comparing the resemblance of the pedal charac- high, because one or more nonlinear equations have to be solved teristic and the computed feature. The usability of the features is iteratively for every nonlinear element in the circuit and every sam- demonstrated using a simple classifier in section 6. Section 7 con- ple of the input data. In addition, the characteristic of every non- cludes this paper. linear circuit element has to be known or assumed to solve the nonlinear equations. An alternate way of emulating analog audio units is system 2. SIGNAL MODEL identification with input/output (I/O) measurements. By sending specifically designed input signals through the device under test This section describes the influence of typical effects, as catego- (DUT) and measuring the output, the influence of the system on rized in Fig. 1, on an input signal x(t). This analysis was done to DAFX-1 Proc. of the 18th Int. Conference on Digital Audio Effects (DAFx-15), Trondheim, Norway, Nov 30 - Dec 3, 2015 Delay model which does not take into account that many delay effects employ some modulation or filtering in the feedback path to create LTI Reverb a certain kind of tonal characteristic. Filter 2.2. Non-Linear Time-Invariant Effects 2.2.1. Compression Distortion Audio Effect NLTI Compression is a non-linear effect reducing the dynamic range of Compression the input signal. Therefore, the input signal x(t) is fed to time- variant variable-gain amplification stage, weighting x(t) with a Flanger gain factor g(t) to produce the output Chorus y(t) = g(t) · x(t): (4) LTV Phaser The variable-gain amplifier can be modeled as an envelope sig- Vibrato nal x+(t) smoothed with a signal-dependent lowpass filter LPAT/RT like Tremolo g(t) = LPAT/RTfx+(t)g; (5) Figure 1: Categorization of typical digital audio effects into linear where LP defines a lowpass filter for the attack (AT) and re- time-invariant (LTI), non-linear time-invariant (NLTI), and linear AT/RT lease (RT) case. Typical choices for the envelope signal x (t) time-variant (LTV). + are the peak signal jx(t)j or the root-mean-square signal xRMS(t), depending on the type of compressor. design proper input signals and select adequate features, calculated from the recorded (output) signals. 2.2.2. Distortion 2.1. Linear Time-Invariant Effects Distortion effects modify the input signal x(t) with a nonlinear function f(x) mapping the level of the input signal x(t) to the 2.1.1. Filter level of the output signal y(t), as shown in In the field of digital audio effects, a filter is a linear time-invariant (LTI) system, which is able to amplify or attenuate certain fre- y(t) = f(x(t)): (6) quency regions of the input signal x(t). The impulse response h(t) defines the characteristic of the filter and is convolved with The shape of the nonlinear function defines the tonal quality of the input signal to produce the filtered output, the effect. Musicians tend to sub-categorize distortion effects in overdrive, distortion, and fuzz in ascending order of nonlinearity. y(t) = x(t) ∗ h(t): (1) For an accurate signal model, representing an analog distortion effect unit, there should be additional input and an output filters. 2.1.2. Reverberation Here they are omitted for the sake of readability. Although, the mathematical representation of the reverberation ef- fect 2.3. Linear Time-Variant Effects y(t) = x(t) ∗ hrev(t); (2) namely the convolution of the input signal with an impulse re- Linear time-variant effects, often called modulation effects, mod- sponse hrev, is very similar to the filter effect, those effects are ulate the input signal in terms of volume, frequency or phase using easy to distinguish from each other. The impulse response of fil- a low frequency oscillator (LFO) controlling the rate of the modu- ters and reverberations differ significantly in length and noisiness lation. and hence can be differentiated with measurements. 2.3.1. Tremolo 2.1.3. Delay Repetitions of an input signal can be achieved using delay pedals. The tremolo effect was introduced in the 1950s by companies like The output signal Fender or Vox. Initially it was an electronic circuit integrated in the guitar amplifier which modulated the volume of the output signal y(t) = x(t) + g · y(t − td) (3) periodically. The modulating signal xLFO(t) is multiplied with the input signal x(t) can be modeled with a direct path and a delay line in the feedback y(t) = xLFO(t) · x(t): (7) path. The delay time td defines the temporal distance between two repetitions while the amplitude of the repetitions is controlled xLFO(t) is a periodic signal having, for example, sinusoidal, trian- with the feedback gain g. It should be noted that this is a simple gular, sawtooth or square-wave characteristic. DAFX-2 Proc. of the 18th Int. Conference on Digital Audio Effects (DAFx-15), Trondheim, Norway, Nov 30 - Dec 3, 2015 2.3.2.

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