A General-Purpose Deep Learning Approach to Model Time-Varying Audio Effects

Home , Leslie speaker

Proceedings of the 22nd International Conference on Digital Audio Effects (DAFx-19), Birmingham, UK, September 2–6, 2019

A GENERAL-PURPOSE DEEP LEARNING APPROACH TO MODEL TIME-VARYING AUDIO EFFECTS

Marco A. Martínez Ramírez, Emmanouil Benetos, Joshua D. Reiss Centre for Digital Music, Queen Mary University of London London, United Kingdom m.a.martinezramirez, emmanouil.benetos, [email protected]

ABSTRACT center frequency is modulated by an LFO or an envelope follower, the effect is commonly called auto-wah. Audio processors whose parameters are modified periodically Delay-line based audio effects, as in the case of flanger and over time are often referred as time-varying or modulation based chorus, are based on the modulation of the length of the delay audio effects. Most existing methods for modeling these type of lines. A flanger is implemented via a modulated comb filter whose effect units are often optimized to a very specific circuit and cannot output is mixed with the input audio. Unlike the phaser, the notch be efficiently generalized to other time-varying effects. Based on and peak frequencies caused by the flanger’s sweep comb filter convolutional and recurrent neural networks, we propose a deep effect are equally spaced in the spectrum, thus causing the known learning architecture for generic black-box modeling of audio pro- metallic sound associated with this effect. A chorus occurs when cessors with long-term memory. We explore the capabilities of mixing the input audio with delayed and pitch modulated copies deep neural networks to learn such long temporal dependencies of the original signal. This is similar to various musical sources and we show the network modeling various linear and nonlinear, playing the same instrument but slightly shifted in time. vibrato time-varying and time-invariant audio effects. In order to mea- is digitally implemented as a delay-line based audio effect, where sure the performance of the model, we propose an objective metric pitch shifting is achieved when periodically varying the delay time based on the psychoacoustics of modulation frequency perception. of the input waveform [3]. We also analyze what the model is actually learning and how the Tremolo is an amplitude modulation effect where an LFO is given task is accomplished. used to directly vary the amplitude of the incoming audio, creat- ing in this way a perceptual temporal fluctuation. A ring modu- 1. INTRODUCTION lator is also based on amplitude modulation, but the modulation is achieved by having the input audio multiplied by a sinusoidal Modulation based or time-varying audio effects involve audio pro- oscillator with higher carrier frequencies. In the analog domain, cessors or effect units that include a modulator signal within their this effect is commonly implemented with a diode bridge, which analog or digital implementation [1]. These modulator signals are adds a nonlinear behavior and a distinct sound to this effect unit. in the low frequency range (usually below 20 Hz). Their wave- Another type of modulation based effect that combines amplitude, forms are based on common periodic signals such as sinusoidal, pitch and spatial modulation is the Leslie speaker, which is imple- squarewave or sawtooth oscillators and are often referred to as a mented by a rotating horn and a rotating woofer inside a wooden Low Frequency Oscillator (LFO). The LFO periodically modu- cabinet. This effect can be interpreted as a combination of tremolo, lates certain parameters of the audio processors to alter the timbre, Doppler effect and reverberation [4]. frequency, loudness or spatialization characteristics of the audio. Most of these effects can be implemented directly in the digi- This differs from time-invariant audio effects which do not change tal domain through the use of digital filters and delay lines. Never- their behavior over time. Based on how the LFO is employed and theless, modeling specific effect units or analog circuits has been the underlying signal processing techniques used when designing heavily researched and remains an active field. This is because the effect units, we can classify modulation based audio effects hardware effect units are characterized by the nonlinearities intro- into time-varying filters such as phaser or wah-wah; delay-line duced by certain circuit components. Musicians often prefer the based effects such as flanger or chorus; and amplitude modulation analog counterparts because the digital implementations may lack effects such as tremolo or ring modulator [2]. this behavior, or because the digital simulations make certain as- The phaser effect is a type of time-varying filter implemented sumptions when modeling specific nonlinearities. through a cascade of notch or all-pass filters. The characteristic Virtual analog methods for modeling such effect units mainly sweeping sound of this effect is obtained by modulating the cen- involve circuit modeling and optimization for specific analog com- ter frequency of the filters, which creates phase cancellations or ponents such as operational amplifiers or transistors. This often enhancements when combining the filter’s output with the input requires assumptions or models that are too specific for a certain audio. Similarly, the wah-wah is based on a bandpass filter with circuit. Such models are also not easily transferable to different a variable center frequency, usually controlled by a pedal. If the effects units since expert knowledge of the type of circuit being Copyright: c 2019 Marco A. Martínez Ramírez, Emmanouil Benetos, Joshua D. modeled is required, i.e. specific linear and nonlinear components. Reiss et al. This is an open-access article distributed under the terms of the Creative Prior to this work, deep learning architectures have not yet Commons Attribution 3.0 Unported License, which permits unrestricted use, distribu- been implemented to model time-varying audio effects. Thus, tion, and reproduction in any medium, provided the original author and source are building on [5, 6], we propose a general-purpose deep learning credited. approach to model this type of audio effects. Using convolutional,

DAFX-1 Proceedings of the 22nd International Conference on Digital Audio Effects (DAFx-19), Birmingham, UK, September 2–6, 2019 recurrent and fully connected layers, we explore how a deep neural nonlinear characteristics of the transistors. network (DNN) can learn the long temporal dependencies which Amplitude modulation effects such as an analog ring modula- characterizes these effect units as well as the possibilities to match tor were modeled in [17], where the diode bridge is emulated as nonlinearities within the audio effects. We include Bidirectional a network of static nonlinearities. [18] modeled the rotating horn Long Short-Term Memory (Bi-LSTM) neural networks and ex- of the Leslie speaker via varying delay-lines, artificial reverber- plore their capabilities when learning time-varying transformations. ation and physical measurements from the rotating loudspeaker. We explore linear and nonlinear time-varying emulation as a content- [19] also modeled the Leslie speaker and achieved frequency mod- based transformation without explicitly obtaining the solution of ulation through time-varying spectral delay filters and amplitude the time-varying system. modulation using a modulator signal. In both Leslie speaker emu- We show the model matching modulation based audio effects lations, various physical characteristics of the effect are not taken such as chorus, flanger, phaser, tremolo, vibrato, tremolo-wah, into account, such as the frequency-dependent directivity of the ring modulator and Leslie speaker. We investigate the capabilities loudspeaker and the effect of the wooden cabinet. of the model when adding further nonlinearities to the linear time- In [20], gray-box modeling was proposed for linear time-varying varying audio effects. Furthermore, we extend the applications audio effects. This differs from white-box modeling, since the of the model by including nonlinear time-invariant audio effects method was based on input-output measurements but the time- with long temporal dependencies such as auto-wah, compressor varying filters were based on knowledge of analog phasers. In this and multiband compressor. Finally, we measure performance of way, phaser emulation was achieved by multiple measurements of the model using a metric based on the modulation spectrum. the impulse response of a cascade of all-pass filters. The paper is structured as follows. In Section 2 we present the Another method to model time-varying audio effects is dis- relevant literature related to virtual analog of modulation based au- cretizing electrical circuit elements via Wave Digital Filters (WDF). dio effects. Section 3 gives details of our model, the modulation The Hammond organ vibrato/chorus was modeled using WDFs in based effect tasks and the proposed evaluation metric. Sections 4 [21], and [22] performed circuit modeling through WDFs to emu- and 5 show the analysis, obtained results, and the respective con- late modulation based effects that use OTAs. clusion.

2. BACKGROUND 2.2. End-to-end deep neural networks End-to-end deep learning is based on the idea that an entire prob- 2.1. Virtual analog modeling of time-varying audio effects lem can be taken as a single indivisible task which must be learned Virtual analog audio effects aim to simulate an effect unit and from input to output. Deep learning architectures using this prin- recreate the sound of an analog reference circuit. Much of the ac- ciple have recently been researched in the music information re- tive research models nonlinear audio processors such as distortion trieval field [23, 24, 25], since the amount of required prior knowl- effects, compressors, amplifiers or vacuum tubes [7, 8, 9]. With re- edge may be reduced and engineering effort minimized by learn- spect to modeling time-varying audio effects, most of the research ing directly from raw audio [26]. Recent work also demonstrated has been applied to develop white-box methods, i.e. in order to the feasibility of these architectures for audio synthesis and audio model the effect unit a complete study of the internal circuit is effects modeling. [27, 28] proposed models that synthesize au- carried out. These methods use circuit simulation techniques to dio waveforms and [29] obtained a model capable of performing characterize various analog components such as diodes, transis- singing voice synthesis. tors, operational amplifiers or integrated circuits. End-to-end deep neural networks for audio effects modeling In [10], phasers implemented via Junction Field Effect Tran- were implemented in [5], where Equalization (EQ) matching was sistors (JFET) and Operational Transconductance Amplifiers (OTA) achieved with convolutional neural networks (CNN). Also, [6] pre- were modeled using circuit simulation techniques that discretize sented a deep learning architecture for modeling nonlinear proces- the differential equations that describe these components. Using sors such as distortion, overdrive and amplifier emulation. The a similar circuit modeling procedure, delay-line based effects are DNN is capable of modeling an arbitrary combination of linear modeled, such as flanger and chorus as implemented with Bucket and nonlinear memoryless audio effects, but does not generalize Brigade Delay (BBD) chips. BBD circuits have been widely used to transformations with long temporal dependencies such as mod- in analog delay-line based effect units and several digital emula- ulation based audio effects. tions have been investigated. [11] emulated BBD devices through circuit analysis and electrical measurements of the linear and nonlinear elements of the integrated circuit. [12] modeled BBDs as 3. METHODS delay-lines with fixed length but variable sample rate. Based on BBD circuitry, a flanger effect was modeled in [13] 3.1. Model via the nodal DK-method. This is a common method in virtual analog modeling [14] where nonlinear filters are derived from the The model is entirely based on the time-domain and operates with differential equations that describe an electrical circuit. In [15], a raw audio as the input and processed audio as the output. It is wah-wah pedal is implemented using the nodal DK-method and divided into three parts: adaptive front-end, latent-space and syn- the method is extended to model the temporal fluctuations intro- thesis back-end. A block diagram can be seen in Fig. 1 and its duced by the continuous change of the pedal. In [16], the MXR structure is described in detail in Table 1. We build on the architec- Phase 90 phaser effect is modeled via a thorough circuit analy- ture from [6], since we incorporate Bi-LSTMs into the latent-space sis and the DK-method. This effect unit is based on JFETs, and and we modify the structure of the synthesis back-end in order to voltage and current measurements were performed to obtain the allow the model to learn nonlinear time-varying transformations.

DAFX-2 Proceedings of the 22nd International Conference on Digital Audio Effects (DAFx-19), Birmingham, UK, September 2–6, 2019

Adaptive Front-end Synthesis Back-end

Input Output audio audio Conv1D Max DNN Conv1D Bi-LSTM Unpool deConv1D Local Pool SAAF SAAF SE

Figure 1: Block diagram of the proposed model; adaptive front-end, Bi-LSTM and synthesis back-end.

Table 1: Detailed architecture of a model with input frame size of 4096 samples and ±4 context frames. X1 = x ∗ W 1 (2) Where X is the feature map after the input audio x is con- Layer Output shape Units Output 1 volved with the kernel matrix W 1. R is the corresponding row Input (9, 4096, 1) . x in X1 for the frequency band decomposition of the current input Conv1D (9, 4096, 32) 32(64) X1 frame x(t). The back-end does not directly receive information Residual (4096, 32) . R from the past and subsequent context frames. The second layer Abs (9, 4096, 32) . . Conv1D-Local (9, 4096, 32) 32(128) . has 32 filters of size 128 and each filter is locally connected. We Softplus (9, 4096, 32) . X2 follow a filter bank architecture since each filter is only applied MaxPooling (9, 64, 32) . Z to its corresponding row in |X1| and so we significantly decrease Bi-LSTM (64, 128) 64 . the number of trainable parameters. This layer is followed by the Bi-LSTM (64, 64) 32 . softplus nonlinearity [30], described by (3). Bi-LSTM (64, 32) 16 . ˆ SAAF (64, 32) 25 Z X2 = softplus(|X1| ∗ W 2) (3) ˆ Unpooling (4096, 32) . X3 Where X2 is the second feature map obtained after the local Multiply (4096, 32) . Xˆ 2 convolution with W 2, the kernel matrix of the second layer. The Dense (4096, 32) 32 . max-pooling operation is a moving window of size N/64 applied Dense (4096, 16) 16 . Dense (4096, 16) 16 . over X2, where the maximum value within each window corre- Dense (4096, 32) 32 . sponds to the output. SAAF (4096, 32) 25 . By using the absolute value as activation function of the first Abs (4096, 32) . . layer and by having larger filters W 2, we expect the front-end to Global Average (1, 32) . . learn smoother representations of the incoming audio, such as en- Dense (1, 512) 512 . Dense (1, 32) 32 . velopes [24]. All convolutions and pooling operations are time Multiply (4096, 32) . Xˆ1 distributed, i.e the same convolution or pooling operation is ap- Add (4096, 32) . Xˆ0 plied to each of the 2 · k + 1 input frames. deConv1D (4096, 1) . yˆ 3.3. Bidirectional LSTMs The latent-space consists of three Bi-LSTM layers of 64, 32, and 3.2. Adaptive front-end 16 units respectively. Bi-LSTMs are a type of recurrent neural net- The front-end performs time-domain convolutions with the incom- work that can access long-term context from both backward and ing audio. It follows a filter bank architecture and is designed to forward directions [31]. Bi-LSTMs are capable of learning long learn a latent representation for each audio effect modeling task. It temporal dependencies when processing timeseries where the con- consists of a convolutional encoder which contains two CNN lay- text of the input is needed [32]. ers, one pooling layer and one residual connection. This residual The Bi-LSTMs process the latent-space representation Z. Z connection is used by the back-end to facilitate the synthesis of the is learned by the front-end and contains information regarding the waveform based on the specific time-varying transformation. 2 · k + 1 input frames. These recurrent layers are trained to reduce In order to allow the model to learn long-term memory depen- the dimension of Z, while also learning a nonlinear modulation ˆ dencies, the input consists of the current audio frame x(t) concate- Z. This new latent representation is fed into the synthesis back- nated with the k previous and k subsequent frames. These frames end in order to reconstruct an audio signal that matches the time- are of size N and sampled with a hop size τ. The input x is de- varying task. Each Bi-LSTM has dropout and recurrent dropout scribed by (1). rates of 0.1 and the first two layers have the hyperbolic tangent as activation function. x = x(t + jτ), j = −k, ..., k (1) The performance of CNNs in regression tasks has improved by using adaptive activation functions [33]. So we add a Smooth The first convolutional layer has 32 one-dimensional filters of Adaptive Activation Function (SAAF) as the nonlinearity for the size 64 and is followed by the absolute value as nonlinear activa- last layer. SAAFs consist of piecewise second order polynomials tion function. The operation performed by the first layer can be which can approximate any continuous function and are regular- described by (2). ized under a Lipschitz constant to ensure smoothness. As shown

DAFX-3 Proceedings of the 22nd International Conference on Digital Audio Effects (DAFx-19), Birmingham, UK, September 2–6, 2019 in [6], SAAFs can be used within deep neural networks to model input and target functions. This means the model is being prepared nonlinearities in audio processing tasks. to reconstruct the input and target data in order to have a better fit- ting when training for the time-varying task. Only during this step, 3.4. Synthesis back-end the unpooling layer of the back-end uses the time positions of the maximum values recorded by the max-pooling operation. The synthesis back-end accomplishes the reconstruction of the tar- Once the model is pretrained, the Bi-LSTM and DNN-SAAF- get audio by processing the current input frame x(t) and the non- SE layers are incorporated into the model, and all the weights of linear modulation Zˆ. The back-end consists of an unpooling layer, the convolutional, recurrent, dense and activation layers are up- a DNN block with SAAF and Squeeze-and-Excitation (SE) [34] dated. Since small amplitude errors are as important as large ones, layers (DNN-SAAF-SE) and a final CNN layer. the loss function to be minimized is the mean absolute error be- The DNN-SAAF-SE block consists of 4 fully connected (FC) tween the target and output waveforms. We explore input size layers of 32, 16, 16 and 32 hidden units respectively. Each FC frames from 1024 to 8192 samples and we always use a hop size layer is followed by the hiperbolic tangent function except for the of 50%. The batch size consisted of the total number of frames per last one, which is followed by a SAAF layer. Overall, each SAAF audio sample. is locally connected and each function consists of 25 intervals be- Adam is used as optimizer and we perform the pretraining for tween −1 to 1. 200 epochs and the supervised training for 500 epochs. In order to The SE blocks explicitly model interdependencies between speed convergence, during the second training step we start with a channels by adaptively scaling the channel-wise information of learning rate of 5·10−5 and we reduce it by 50% every 150 epochs. feature maps [34]. The SE dynamically scales each of the 32 chan- We select the model with the lowest error for the validation subset. nels and follows the structure from [35]. It consists of a global average pooling operation followed by two FC layers of 512 and 32 hidden units respectively. The FC layers are followed by a rec- 3.6. Dataset tifier linear unit (ReLU) and sigmoid activation functions accord- Modulation based audio effects such as chorus, flanger, phaser, ingly. Since the feature maps of the model are based on time- tremolo and vibrato were obtained from the IDMT-SMT-Audio- domain waveforms, we incorporate an absolute value layer before Effects dataset [36]. It corresponds to individual 2-second notes the global average pooling operation. and covers the common pitch range of various 6-string electric gui- The back-end matches the time-varying task by the following tars and 4-string bass guitars. steps. First, a discrete approximation of Z (Xˆ 3) is obtained by ˆ The recordings include the raw notes and their respective ef- an upsampling operation. Then the feature map X2 is the result fected versions for 3 different settings for each effect. For our the element-wise multiplication of the residual connection R and ˆ experiments, for each of the above effects, we only use the setting X3. This can be seen as a frequency dependent amplitude mod- #2 from where we obtained the unprocessed and processed audio ulation between the learned modulator Z and the frequency band for bass guitar. In addition, processing the bass guitar raw audio, decomposition R. we implemented an auto-wah with a peak filter whose center fre- 500 3 5 ˆ ˆ quency ranges from Hz to kHz and modulated by a Hz X2 = X3 · R (4) sinusoidal. The feature map Xˆ 1 is obtained when the nonlinear and channel- Since the previous audio effects are linear time-varying, we wise scaled filters from the DNN-SAAF-SE block are applied to further test the capabilities of the model by adding a nonlinearity the modulated frequency band decomposition Xˆ 2. Then, Xˆ 1 is to each of these effects. Thus, using the bass guitar wet audio, we added back to Xˆ 2, acting as a nonlinear delay-line. applied an overdrive (gain= +10dB) after each modulation based effect. Xˆ0 = Xˆ 1 + Xˆ 2 (5) We also use virtual analog implementations of a ring modula- The last layer corresponds to the deconvolution operation, which tor and a Leslie speaker to process the electric guitar raw audio. can be implemented by transposing the first layer transform. This layer is not trainable since its kernels are transposed versions of W 1. In this way, the back-end reconstructs the audio waveform in 0.07 chorus the same manner that the front-end decomposed it. The complete 0.06 waveform is synthesized using a hanning window and constant flanger 0.05 phaser overlap-add gain. tremolo All convolutions are along the time dimension and all strides 0.04 vibrato are of unit value. The models have approximately 300k trainable mae 0.03 parameters, which, within a deep learning context, represents a model that is not very large or difficult to train. 0.02

0.01 3.5. Training 0.00 1024 2048 4096 8192 The training of the model is performed in two steps. The ﬁrst step input frame size is to train only the convolutional layers for an unsupervised learning task, while the second step consists of an end-to-end supervised learning task based on a given time-varying target. During the ﬁrst step only the weights of Conv1D and Conv1D-Local are Figure 2: mae values for linear time-varying tasks with different trained and both the raw audio x(t) and wet audio y(t) are used as input size frames.

DAFX-4 Proceedings of the 22nd International Conference on Digital Audio Effects (DAFx-19), Birmingham, UK, September 2–6, 2019

0.03 0.02

mae 0.01 0.00 chorus flanger phaser tremolo vibrato auto-wah ring-modulatorleslie_left leslie_rightchorus_odflanger_odphaser_odtremolo_odvibrato_odauto-wah_odauto-wah_efcompressormb-compressor

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0.00 chorus flanger phaser tremolo vibrato auto-wah ring-modulatorleslie_left leslie_rightchorus_odflanger_odphaser_odtremolo_odvibrato_odauto-wah_odauto-wah_efcompressormb-compressor

Figure 3: mae and msed values with the test dataset for all the time-varying tasks. od, ef and mb mean overdrive, envelope follower and multiband respectively.

The ring modulator implementation1 is based on [17] and we use term knowledge of temporal fluctuation patterns [37]. We pro- a modulator signal of 5 Hz. The Leslie speaker implementation2 pose the modulation spectrum euclidean distance (msed), which is is based on [18] and we model each of the stereo channels. based on the audio features from [38] and [39] and is defined as Finally, we also explore the capabilities of the model with non- follows: linear time-invariant audio effects with long temporal dependencies, such as compressors and auto-wah. We use the compressor • A Gammatone filter bank is applied to the target and output and multiband compressor from SoX3 to process the electric guitar entire waveforms. In total we use 12 filters, with center raw audio. The settings of the compressor are as follows: attack frequencies spaced logarithmically from 26 Hz to 6950 Hz. time 10 ms, release time 100 ms, knee 1 dB, ratio 4:1 and threshold • The envelope of each filter output is calculated via the mag- -40 dB. The multiband compressor has 2 bands with a crossover nitude of the Hilbert transform and downsampled to 400 frequency of 500 Hz, attack time: 5 ms and 625 µs, decay time: Hz. 100 ms and 12.5 ms, knee: 0 dB and 6 dB, ratio: 3:1 and 6:1 and threshold: −30 dB and −60 dB. • A Modulation filter bank is applied to each envelope. In Similarly, we use an auto-wah implementation4 with an enve- total we use 12 filters, with center frequencies spaced loga- lope follower and a peak filter which center frequency modulates rithmically from 0.5 Hz to 100 Hz. between 500 Hz to 3 kHz. • The Fast Fourier Transform (FFT) is calculated for each For each time-varying task we use 624 raw and effected notes modulation filter output of each Gammatone filter. The and both the test and validation samples correspond to 5% of this energy is normalized by the DC value and summarized in subset each. The recordings were downsampled to 16 kHz and the following bands: 0.5-4 Hz, 4.5-10 Hz, 10.5-20 Hz and amplitude normalization was applied with exception to the time- 20.5-100 Hz. invariant audio effects. • The msed metric is the mean euclidean distance between the energy values at these 4 bands. 3.7. Evaluation Two metrics were used when testing the models with the various test subsets. Since the mean absolute error depends on the am- 4. RESULTS & ANALYSIS plitude of the output and target waveforms, before calculating this error, we normalize the energy of the target and the output and First, we explore the capabilities of Bi-LSTMs to learn long-term define it as the energy-normalized mean absolute error (mae). temporal dependencies. Fig. 2 shows the mae results of the test We also propose an objective metric which mimics human per- dataset for different input frame sizes and various linear time- ception of amplitude and frequency modulation. The modulation varying tasks. The most optimal results are with an input size spectrum uses time-frequency theory integrated with the psychoa- of 4096 samples, since shorter frame sizes represent a higher er- coustics of modulation frequency perception, thus, providing long- ror and 8192 samples do not represent a significant improvement. Since the average modulation frequency in our tasks is 2 Hz, for 1https://github.com/nrlakin/robot_voice/blob/master/robot.py each input size we select a k that covers one period of this mod- 2https://ccrma.stanford.edu/software/snd/snd/leslie.cms ulator signal. Thus, for the rest of our experiments, we use an 3http://sox.sourceforge.net/ input size of 4096 samples and k = 4 for the number of past and 4https://github.com/lucieperrotta/ASP subsequent frames.

DAFX-5 Proceedings of the 22nd International Conference on Digital Audio Effects (DAFx-19), Birmingham, UK, September 2–6, 2019

0.75 0.50 0.25 0.00 0.25 0.50 amplitude 0.75 1.00 0.00 0.05 0.10 0.15 0.20 0.25 time (b) (c)

10 1

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10 5 input magnitude 6 10 target 10 7 output

101 102 103 104 frequency (Hz) (a) (d) (e)

Figure 4: Various internal plots for the test dataset of the tremolo modeling task. 4a) Input, target and output frames of 4096 samples and their respective FFT magnitudes. 4b) For the input frame x(t), respective 8 rows from R. 4c) Following the ﬁlter bank architecture, respective 8 rows from X2. 4d) From Zˆ, corresponding 8 modulator signals learned by the Bi-LSTM layer. 4e) In the same manner, 8 rows from Xˆ 0, which is the input to the deconvolution layer prior to obtaining the output frame yˆ(t). Vertical axes in 4b)-4e) are unitless and horizontal axes correspond to time.

The training procedures were performed for each type of time- values. Although the mae increased, the model does not signif- varying and time-invariant audio effect. Then, the models were icantly reduce performance and is able to match the combination tested with samples from the test dataset and the audio results are of nonlinear and modulation based audio effects. available online5. Fig. 3 shows the mae and msed for all the test Much more complicated time-varying tasks, such as the ring subsets. To provide a reference, the mean mae and msed values be- modulator and Leslie speaker virtual analog implementations were tween input and target waveforms are 0.15 and 0.11 respectively. also successfully modeled. This represents a significant result, It can be seen that the model performed well on each audio effect since these implementations include nonlinear modulation; ring modeling task. Overall, the model achieved better results with am- modulator, or varying delay lines together with artificial reverber- plitude modulation and time-varying filter audio effects, although ation and Doppler effect simulation; and the Leslie speaker. delay-line based effects were also successfully modeled. Lastly, the model is also able to perform linear and nonlinear Fig. 4 visualizes the functioning of the model for the tremolo time-invariant modeling. The long temporal dependencies of an task. It shows how the model processes the input frame x(t) into envelope driven auto-wah, compressor and multiband compres- the different frequency maps X1 and X2, learns a modulator sig- sor are succesfully modeled. Furthermore, in the latter case, the nal Zˆ, and applies the respective amplitude modulation. This lin- crossover filters are also matched. The msed may not be relevant ear time-varying audio effect is easy to interpret. For more com- for these effects, but the low mae values represent that the model plex nonlinear time-varying effects, a more in-depth analysis of also performs well here. the model is required. For selected linear and nonlinear time-varying tasks, Fig. 5 shows the input, target, and output waveforms together with their 5. CONCLUSION respective modulation spectrum. In the time-domain, it is evident that the model is matching the target waveform. From the modu- In this work, we introduced a general-purpose deep learning archi- lation spectrum it is noticeable that the model introduces different tecture for modeling audio effects with long temporal dependen- modulation energies into the output which were not present in the cies. Using raw audio and a given time-varying task, we explored input and which closely match those of the respective targets. the capabilities of end-to-end deep neural networks to learn low- The task becomes more challenging when a nonlinearity is frequency modulations and to process the audio accordingly. The added to a linear time-varying transformation. Fig. 5d depicts re- model was able to match linear and nonlinear time-varying au- sults for the phaser-overdrive task. Given the large overdrive gain dio effects, time-varying virtual analog implementations and time- the resulting audio has a lower-frequency modulation. It can be invariant audio effects with long-term memory. seen that the model introduces modulations as low as 0.5 Hz. But Other white-box or gray-box modeling methods suitable for the waveform is not as smooth as the target, hence the larger mae these time-varying tasks would require expert knowledge such as specific circuit analysis and discretization techniques. Moreover, 5https://mchijmma.github.io/modeling-time-varying/ these methods can not easily be extended to other time-varying

DAFX-6 Proceedings of the 22nd International Conference on Digital Audio Effects (DAFx-19), Birmingham, UK, September 2–6, 2019

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1.0 6950 1.0 6950 5070 5070 3683 3683 0.5 2659 0.5 2659 1903 1903 0.0 1345 0.0 1345 933 933 629 629

amplitude 0.5 404 amplitude 0.5 404 238 238 116 116 1.0 25 1.0 25 Gammatone Freq (Hz) Gammatone Freq (Hz) 1.0 1.0 6950 6950 5070 5070 0.5 3683 0.5 3683 2659 2659 1903 1903 0.0 1345 0.0 1345 933 933 0.5 629 0.5 629 404 404 238 238 1.0 116 1.0 116 25 25 0.0 0.5 1.0 1.5 2.0 0.5 4 10 20 100 0.0 0.5 1.0 1.5 2.0 0.5 4 10 20 100 time Modulation Freq (Hz) time Modulation Freq (Hz) (a) (b)

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Figure 5: Results with the test dataset for the following time-varying tasks: 5a) chorus, 5b) ring modulator, 5c) Leslie speaker (right channel) and 5d) phaser-overdrive. For each subfigure and from top to bottom: input, target and output waveforms and respective modulation spectrum plots. tasks, and assumptions are often made regarding the nonlinear be- The generalization can also be studied more thoroughly, since havior of certain components. To the best of our knowledge, this the model learns to apply the specific transformation to the audio work represents the first black-box modeling method for linear and of a specific musical instrument, such as the electric guitar or the nonlinear, time-varying and time-invariant audio effects. It makes bass guitar. In addition, since the model strives to learn long tem- less assumptions about the audio processor target and represents poral dependencies with shorter input size frames, and also needs an improvement of the state-of-the-art in audio effects modeling. past and subsequent frames, more research is needed on how to Using a small amount of training examples we showed the adapt this architecture to real-time implementations. model matching chorus, flanger, phaser, tremolo, vibrato, auto- Real-time applications would benefit significantly from the ex- wah, ring modulator, Leslie speaker and compressors. We pro- ploration of recurrent neural networks to model transformations posed an objective perceptual metric to measure the performance that involve long-term memory without resorting to large input of the model. The metric is based on the euclidean distance be- frame sizes and the need for past and future context frames. Al- tween the frequency bands of interest within the modulation spec- though the model was able to match the artificial reverberation of trum. We demonstrated that the model processes the input audio the Leslie speaker implementation, a thorough exploration of re- by applying different modulations which closely match with those verberation modeling is needed, such as plate, spring or convolu- of the time-varying target. tion reverberation. In addition, since the model is learning a static representation of the audio effect, ways of devising a parametric Perceptually, most output waveforms are indistinguishable from model could also be explored. Finally, applications beyond virtual their target counterparts, although there are minor discrepancies at analog can be investigated, for example, in the field of automatic the highest frequencies and noise level. This could be improved by mixing the model could be trained to learn a generalization from using more convolution filters, which means a higher resolution in mixing practices. the filter bank structures [6]. Moreover, as shown in [5], a cost function based on time and frequency can be used to improve this frequency related issue, though listening tests may be required.

DAFX-7 Proceedings of the 22nd International Conference on Digital Audio Effects (DAFx-19), Birmingham, UK, September 2–6, 2019

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