# Neural synthesis of footsteps sound effects
with generative adversarial networks

###### Abstract

Footsteps are among the most ubiquitous sound effects in multimedia
applications. There is substantial research into understanding the acoustic
features and developing synthesis models for footstep sound effects. In this
paper, we present a first attempt at adopting neural synthesis for this task.
We implemented two GAN-based architectures and compared the results with real
recordings as well as six traditional sound synthesis methods. Our
architectures reached realism scores as high as recorded samples, showing
encouraging results for the task at hand.

Index Terms—  footsteps, neural synthesis, sound effects, GAN

##  1 Introduction

When sound designers are given the task of creating sound effects for movies,
video games or radio shows, they have essentially two options: pre-recorded
samples or procedural audio. In the first case, they usually rely on large
libraries of high quality audio recordings, from which they have to select,
edit and mix samples for each event they need to sonify. For a realistic
result, especially in video games where the same actions are repeated many
times, several samples are selected for every event and randomised during
action. This creates challenges in terms of memory requirements, assets
management and implementation time.

Alternatively, procedural audio [1] aims to synthesise sound effects in real
time, based on a set of input parameters. In the context of video games, these
parameters might come from the specific interaction of a character with the
environment. This approach presents challenges in terms of development of
procedural models which can synthesise high quality and realistic audio, as
well as finding the right parameters values for each sound event.

Footsteps sounds are a typical example of the challenges of sound design. It
is an omnipresent sound, which is generally fairly repetitive, but on which
the human ear is being constantly trained. In fact, it is possible for a
subject to identify from recorded or synthesised footsteps, things like:
gender [2], emotions [3], posture [4], identity [5], ground materials [6], and
type of locomotion [7].

Thus, it is not surprising that researchers put substantial efforts into
understanding the acoustic features [8] and developing synthesis models of
footsteps sounds. Cook [9] made a first attempt at synthesising footsteps on
different surfaces, based on his previous work on physically-informed
stochastic models (PhISM) [10]. Another physically-informed model - based on
using a stochastic controller to drive sums of microscopic impacts - was
proposed by Fontana and Bresin [11]. DeWitt and Bresin [12] proposed a model
that included the user’s emotion parameter. Farnell [13] instead, developed a
procedural model by studying the characteristics of locomotion in primates. In
[14] Turchet developed physical and physically-inspired models coupled with
additive synthesis and signals multiplication.

To this day, there has not yet been an attempt at exploring the use of neural
networks for the synthesis of footsteps sounds although there is substantial
literature exploring neural synthesis of broadband impulsive sounds, such as
drums samples, which have some similarities to footsteps. One of the first
attempts was in [15], where Donahue _et al._ developed WaveGAN - a generative
adversarial network for unconditional audio synthesis. Another example of
neural synthesis of drums is [16], where the authors used a Progressive
Growing GAN. Variational autoencoders [17] and U-Nets [18] have also been used
for the same task. But, the application of recent developments to neural
synthesis of sound effects is yet to be explored. We could only find one other
work [19], related to the present study, where the authors focused on
synthesis of knocking sounds with emotional content using a conditional
WaveGAN.

In this paper we describe the first attempt at neural synthesis of footsteps
sound effects. In Section 2, we propose a hybrid architecture that improves
the quality and better approximates the real data distribution, with respect
to a standard conditional WaveGAN. Objective evaluation of this architecture
is provided in Section 3. Section 4 reports on the first listening test that
compares “traditional” and neural synthesis models on the task at hand.
Discussion and conclusions are given in Section 5.

##  2 Architecture

###  2.1 Data

To train our models we curated a small dataset (81 samples) using free
footsteps sounds available on the Zapsplat
website111https://www.zapsplat.com/sound-effect-packs/footsteps-in-high-heels.
These are high quality samples, recorded by Foley artists using a single type
of shoes (women, high heels) on seven surfaces (carpet, deck, metal, pavement,
rug, wood and wood internal). The samples were converted to WAV file format,
resampled at 16kHz, time aligned and normalised to -6dBFS.

###  2.2 Generator

The original WaveGAN generator [15] was designed to synthesise 16384 samples
($\sim$1s at 16kHz sampling frequency); and afterwards extended to 32768 or
65536 samples222https://github.com/chrisdonahue/wavegan ($\sim$2s and $\sim$4s
at 16kHz). We adapted the architecture to synthesise 8192 samples, which is
sufficient to capture all footsteps samples in our dataset. As shown in Fig.
1, the generator expands the latent variable $z$ to the final audio output
size. After reshaping and concatenation with the conditioning label [19, 20],
the output is synthesised by passing the input through 5 upsampling 1-D
convolutional layers. Upsampling is obtained by: zero-stuffing or nearest
neighbour, linear or cubic interpolation. Zero-stuffing plus 1-D convolution
is equivalent to using 1-D transposed convolution with stride equal to the
upsampling rate. In the other cases, we split the operation into upsampling
and 1-D convolution with stride of 1. Every convolutional layer uses a kernel
size of 25, as in the original design. Differently from the original, we
included batch normalisation layers after each convolution. With the exception
of the last layer, the number of output channels is halved at each convolution
layer.

###  2.3 Discriminator

Recent results in the field of neural speech synthesis [21, 22, 23, 24, 25,
26] have shown how GAN-based vocoders are capable of reaching state-of-the-art
results in terms of mean opinion scores. You _et al._ [27] hypothesised that
this success is not related to the specific design choices or training
strategies; they identified it in the multi-resolution discriminating
framework. In their work, the authors trained 6 different generators using the
same discriminator (Hifi-GAN - [26]) reaching very similar performance
independently of the specific generator.

We experimented with a similar approach by implementing conditional versions
of WaveGAN and HiFi-GAN discriminators. Our WaveGAN discriminator was again
adapted to 8192 samples of the original architecture, based on 5 1-D
convolutional layers with stride of 4 (see Fig. 2). The HiFi-GAN discriminator
is made of two separate discriminators (multi-scale and multi-period) each of
which is made of several sub-discriminators that work with inputs of different
resolutions. The multi-scale discriminator works on raw audio, $\times 2$
average-pooled and $\times 4$ average-pooled audio (i.e., a downsampled and
smoothed version of the original signal). The multi-period discriminator works
on “equally spaced samples of an input audio; the space is given as period
$p$”. The periods are set in the original model to 2, 3, 5, 7 and 11.

Fig. 1: WaveGAN generator. Fig. 2: WaveGAN discriminator.

###  2.4 Loss and Training Procedure

We trained the two architectures - WaveGAN generator and discriminator
(referred to as WaveGAN), WaveGAN generator and HiFi-GAN discriminator
(referred to as HiFi-WaveGAN) - using different paradigms.

In the first case we opted for a Wasserstein GAN with gradient penalty [28]
(WGAN-GP), which has been shown to improve training stability and help
convergence towards a minimum which approximates better the real data
distribution as well as the synthesised samples’ quality. When training a
WGAN-GP the discriminator’s weights were updated several times for each update
of the generator; we followed the standard approach of 5 to 1 updates ratio.

For HiFi-WaveGAN we followed the approach suggested in [26], opting for least
squares GAN (LS-GAN) [29], where the binary cross-entropy terms of the
original GAN [30] are replaced with least squares losses. [26] included
additional losses for the generator; specifically, a mel-spectrogram loss and
a feature matching loss. The mel-spectrogram loss measures the $\ell
1$-distance between the mel-spectrogram of a synthesised and a ground truth
waveforms. We discarded this term since, differently from HiFi-GAN, our
generator was not designed and trained to synthesise waveforms from ground-
truth spectrograms. However, we kept the feature matching loss, which measures
the $\ell 1$-distance of the features extracted at every level of each sub-
discriminator, between real and generated samples. See [26] for a more
detailed description of each loss term. The final losses for our HiFi-WaveGAN
were:

$\displaystyle\mathcal{L}_{G}$
$\displaystyle=\mathcal{L}_{Adv}(G;D)+\lambda_{fm}\mathcal{L}_{FM},$
$\displaystyle\mathcal{L}_{D}$ $\displaystyle=\mathcal{L}_{Adv}(D;G),$

where $\mathcal{L}_{G}$ and $\mathcal{L}_{D}$ are the generator and
discriminator total losses with $\mathcal{L}_{Adv}(G;D)$ and
$\mathcal{L}_{Adv}(D;G)$ the adversarial loss terms for generator and
discriminator and $\lambda_{fm}\mathcal{L}_{FM}$ the feature matching loss.
Both architectures were trained for 120k batches, with a batch size of 16 and
a learning rate of 0.0001. WaveGAN used Adam while HiFi-WaveGAN used AdamW
optimisers.

##  3 Objective Evaluation

Fig. 3: Graphs representing Fréchet Audio Distance (A), Kernel Inception
Distance (B) and Maximum Mean Discrepancy (C) for our models.

There are no formalised methods to reliably evaluate the quality and diversity
of synthesised audio, but there are several metrics which are commonly adopted
to analyse and compare neural synthesis models. We followed a similar approach
to [16] for objective evaluation, where the authors relied on Inception Score
(IS), Kernel Inception Distance (KID) and Fréchet Audio Distance (FAD). We
also relied on the maximum mean discrepancy (MMD) [31] as a measure of
similarity between real and synthesised samples, using the same formulation
adopted in [32], and computed the MMD using the $\ell 1$-distance between
OpenL3 embeddings [33] (env, mel128,
512)333https://github.com/torchopenl3/torchopenl3.

To analyse whether our models were capable of learning the real data
distribution - and to verify that they were not affected by overfitting or
mode collapse - we used principal components analysis (PCA) on the OpenL3
embeddings of real and synthesised samples. We generated 1000 samples for each
class and plot the results of PCA in Fig. 4. It can be seen that HiFi-WaveGAN
reached a better approximation of the real data without collapsing into
synthesising only samples that were seen during training. However, there is
currently no way to establish a correlation between distance in the embedding
space with distance in terms of human perception. It is therefore not possible
to comment on the diversity of synthesised samples from these results.

Instead, we used IS, which gave us a way to measure diversity and semantic
discriminability. This measure ranges from 1 to $n$ (with $n$ number of
classes) and it is maximised for models which can generate samples for all
possible classes and that are classified with high confidence. As classifier,
we used the same Inception Net variant developed in [16] and adapted it to our
domain. We trained the model on a separate dataset (Zapsplat Misc) obtained by
scraping all the freely available footsteps samples on the Zapsplat website
444https://www.zapsplat.com and organised them into 5 classes depending on the
surface material (carpet/rug, deck/boardwalk, metal, pavement/concrete,
wood/wood internal). Likewise, we merged the training (Zapsplat Heels) and
generated data into the same 5 classes by grouping carpet/rug and wood/wood
internal samples together.

We trained the Inception Net for 100 epochs, reaching 86% validation accuracy.
The results are shown in Table 1. It is interesting to notice how the IS for
HiFi-WaveGAN is slightly higher then it is for the training data (Zapsplat
Heels). Assuming that the synthesised data cannot reach a higher semantic
discriminability than the data it learned from, the score should be related to
diversity. Which in turn might depend on two factors. First, we evaluated IS
on a number of Hifi-WaveGAN samples orders of magnitudes greater than the
training data (3500 versus 81). Excluding the case of mode collapse, this will
inherently carry higher diversity. And second, Hifi-WaveGAN is actually able
to synthesise samples not seen during training, and could therefore lead to a
more diverse set. However, it is not possible to comment on the “perceptual”
diversity of the generated samples based on IS only.

Fig. 3 shows the other metrics we adopted to compare real (Misc and Heels) and
synthesised (HiFi and Wave) data. For clarity, we represent FAD, KID and MMD
on 3 graphs, where the distance between nodes is roughly proportional to each
metric values (written on the edges). The 3 metrics, which are based on
comparing embeddings distributions (VGG-ish for FAD, Inception for KID and
OpenL3 for MMD), all depict a similar picture where HiFi-WaveGAN seems to
better approximate the training data. FAD and KID also place HiFi-WaveGAN
samples nearer to the Misc dataset, again suggesting that the model is capable
of synthesising samples with greater diversity than the training data. FAD,
correlating with human judgement, is also a measure of the perceived quality
of individual sounds (the lower the FAD, the higher the quality). In our case,
HiFi-WaveGAN also seems to score higher in terms of quality.

Dataset | IS
---|---
Zapsplat Misc | 4.139
Zapsplat Heels | 3.221
HiFi-WaveGAN | 3.411
WaveGAN | 3.232
Table 1: Inception Score for training and generated data.

Fig. 4: Scatter plots of principal components analysis on OpenL3 embeddings
for: A) Training Data, B) HiFi-WaveGAN and C) WaveGAN.

##  4 Subjective Evaluation

For the subjective evaluation we followed a similar paradigm to [34]. The Web
Audio Evaluation Tool [35] was adopted to run an audio perceptual evaluation
(APE) [36]; a multi-stimulus test in which a participant was presented with a
series of samples to be compared and rated on a continuous scale from 0 to 1.
On this scale, 4 reference values (very unrealistic, somewhat unrealistic,
somewhat realistic, very realistic) were given at the 0, 0.33, 0.66 and 1
points, respectively. Eight synthesis methods were compared to real
recordings:

  1. 1.

Procedural model 1 (PM1) by Fontana and Bresin [11]

  2. 2.

Procedural model 2 (PM2) by Farnell [13]
555http://aspress.co.uk/sd/practical26.html

  3. 3.

Procedural model 3 (PM3) from Nemisindo [37] 666https://nemisindo.com/

  4. 4.

Sinusoidal plus stochastic (SPS) by Amatriain _et al._ [38]
777https://www.dafx.de/DAFX_Book_Page_2nd_edition/chapter10.html

  5. 5.

Statistical modelling (STAT) by McDermott _et al._[39]
888https://mcdermottlab.mit.edu/downloads.html

  6. 6.

Additive synthesis (ADD) by Verron _et al._[40]
999http://www.charlesverron.com/spad.html

  7. 7.

WaveGAN (WAVE)

  8. 8.

HiFi-WaveGAN (HIFI)

To present a more realistic and reliable scenario we prepared 10 s long walks
by concatenating single samples. We started from real recordings and chose -
through informal listening - the time interval between samples that gave the
most realistic result. The same pace was then replicated for all the other
synthesis methods. We prepared a total of 10 series of 9 walks (1 for each
synthesis method plus the real recordings); and presented each participant
with 5 of these series to be able to compare many different conditions (i.e.,
shoe types and surface materials) while keeping the test short.

###  4.1 Results

A total of 19 participants took part in the online
test101010http://webprojects.eecs.qmul.ac.uk/mc309/FootEval/
test.html?url=tests/ape_footsteps.xml. Of these, 10 identified as male, 6 as
female and 3 preferred not to indicate their gender. 3 participants were
excluded since they had no previous experience with critical listening tests.
Of the remaining participants, all but 1 had experience as: musicians (15 out
of 16, $\mu=9.7$, $\sigma=7.6$ and max $=23$ years), sound engineers (11 out
of 16, $\mu=4.6$, $\sigma=7.6$, max $=15$ years) and sound designers (7 out of
16, $\mu=1.7$, $\sigma=2.9$, max $=10$ years). We also enquired about the
headphone models and verified that all participants used good quality devices.

Two criteria were adopted to judge the reliability of each rating. We used one
of the procedural models (PM1) as an anchor, and excluded all cases where this
model was rated above 0.1. Also, we considered real recordings to be the
reference, and excluded all cases where they were rated below 0.5.

Final results are shown in Fig. 5. Together with the anchor (PM1), Farnell’s
model (PM2), as well as statistical and sinusoidal modelling, are the lowest
rated. This result is not surprising since Farnell’s procedural model is a
fairly basic implementation, which lacks the necessary output quality.
Statistical modelling is usually adopted for texture synthesis, and also in
those situations, it shows good results only for very specific cases (see [39,
34]). Also, sinusoidal modelling is more suitable for harmonic sounds (e.g.
musical instruments) where the broadband, noisy components play a minor role
in the overall spectrum. Nemisindo (PM3), being a more recent and advanced
procedural model, scores higher than the other two; and additive synthesis, by
synthesising sounds as a sum of five core elements (modal impact, noisy
impact, chirped impact, band-limited noise, equalised noise), is rated even
higher. Both WaveGAN and HiFi-WaveGAN score as high as the reference. This
result shows that the two neural synthesis models manage to capture all the
important details of the training data, and allow the generated samples to
reach synthesis quality and realism comparable to recorded audio.

Fig. 5: Results of the subjective evaluation.

##  5 Discussion

We presented a first attempt at neural synthesis of footsteps - one of the
most common and challenging sound effects in sound design. In this work, two
GANs architectures were implemented: a standard conditional WaveGAN, and a
hybrid consisting of a conditional WaveGAN generator and a conditional HiFi-
GAN discriminator. The hybrid architecture improves the audio quality of
generated samples and better approximates the training data distribution.
Differently from what is commonly suggested in the literature, upsampling with
zero-stuffing gave us better results than nearest neighbour interpolation. The
same is true for linear or cubic interpolation.

Both objective and subjective evaluation of results were conducted. It is not
common for neural synthesis methods to be compared with “traditional”
synthesis algorithms in a listening test, which makes it impossible to
establish whether a neural synthesis method can actually reach state-of-the-
art results. In this work, we compared the two architectures with 6 other
methods as well as recorded samples. The two architectures reached “realism”
ratings as high as real sounds. Although, from informal listening tests, we
would have expected a greater difference in the ratings. In fact, samples
synthesised by WaveGAN are affected by a perceivable amount of background
noise and distortion, while the opposite is true for HiFi-WaveGAN
111111https://mcomunita.github.io/hifi-wavegan-footsteps_page/.

This opens questions about the definition of realism of sound effects, to what
extent audio quality correlates with perceived realism, and what other aspects
play a significant role in such judgements. Following work will focus on
increasing the degrees of control and diversity of synthesised samples, while
retaining the audio quality.

##  6 Acknowledgements

Funded by UKRI and EPSRC as part of the “UKRI CDT in Artificial Intelligence
and Music”, under grant EP/S022694/1.

## References

  * [1] A. Farnell,  Designing sound,  Mit Press, 2010.
  * [2] X. Li, R.J. Logan, et al.,  “Perception of acoustic source characteristics: Walking sounds,”  J. of the Acoustical Society of America, vol. 90, no. 6, 1991.
  * [3] B. Giordano and R. Bresin,  “Walking and playing: What’s the origin of emotional expressiveness in music,”  in Int. Conf. Music Perception and Cognition, 2006.
  * [4] R.E. Pastore, J.D. Flint, et al.,  “Auditory event perception: The source—perception loop for posture in human gait,”  Perception & Psychophysics, vol. 70, no. 1, 2008.
  * [5] K. Makela, J. Hakulinen, et al.,  “The use of walking sounds in supporting awareness,”  in Int. Conf. on Auditory Display, 2003.
  * [6] R. Nordahl, S. Serafin, et al.,  “Sound synthesis and evaluation of interactive footsteps for virtual reality applications,”  in IEEE Virtual Reality Conference, 2010.
  * [7] R. Bresin and S. Dahl,  “Experiments on gestures: walking, running, and hitting,”  The sounding object, 2003.
  * [8] L. Turchet, D. Moffat, et al.,  “What do your footsteps sound like? an investigation on interactive footstep sounds adjustment,”  Applied Acoustics, vol. 111, 2016.
  * [9] P.R. Cook,  “Modeling bill’s gait: Analysis and parametric synthesis of walking sounds,”  in AES Conf.: Virtual, synthetic, and entertainment audio, 2002\.
  * [10] P.R. Cook,  “Physically informed sonic modeling (phism): Synthesis of percussive sounds,”  Computer Music J., vol. 21, no. 3, 1997.
  * [11] F. Fontana and R. Bresin,  “Physics-based sound synthesis and control: crushing, walking and running by crumpling sounds,”  in Colloquium on Musical Informatics, 2003.
  * [12] A. DeWitt and R. Bresin,  “Sound design for affective interaction,”  in Int. Conf. on Affective Computing and Intelligent Interaction, 2007.
  * [13] A.J. Farnell,  “Marching onwards: procedural synthetic footsteps for video games and animation,”  in Pure Data Conv., 2007.
  * [14] L. Turchet,  “Footstep sounds synthesis: design, implementation, and evaluation of foot–floor interactions, surface materials, shoe types, and walkers’ features,”  Applied Acoustics, vol. 107, 2016.
  * [15] C. Donahue, J. McAuley, et al.,  “Adversarial audio synthesis,”  arXiv preprint arXiv:1802.04208, 2018.
  * [16] J. Nistal, S. Lattner, et al.,  “Drumgan: Synthesis of drum sounds with timbral feature conditioning using generative adversarial networks,”  arXiv preprint arXiv:2008.12073, 2020.
  * [17] C. Aouameur, P. Esling, et al.,  “Neural drum machine: An interactive system for real-time synthesis of drum sounds,”  arXiv preprint arXiv:1907.02637, 2019.
  * [18] A. Ramires, P. Chandna, et al.,  “Neural percussive synthesis parameterised by high-level timbral features,”  in IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 2020\.
  * [19] R.A. Barahona and S. Pauletto,  “Synthesising knocking sound effects using conditional wavegan,”  in Sound and Music Computing Conf., 2020.
  * [20] C.Y. Lee, A. Toffy, et al.,  “Conditional wavegan,”  arXiv preprint arXiv:1809.10636, 2018.
  * [21] K. Kumar, R. Kumar, et al.,  “Melgan: Generative adversarial networks for conditional waveform synthesis,”  arXiv preprint arXiv:1910.06711, 2019.
  * [22] R. Yamamoto, E. Song, et al.,  “Parallel wavegan: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram,”  in IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 2020\.
  * [23] W. Jang, D. Lim, et al.,  “Universal melgan: A robust neural vocoder for high-fidelity waveform generation in multiple domains,”  arXiv preprint arXiv:2011.09631, 2020.
  * [24] E. Song, R. Yamamoto, et al.,  “Improved parallel wavegan vocoder with perceptually weighted spectrogram loss,”  in IEEE Spoken Language Technology Workshop, 2021.
  * [25] J. Yang, J. Lee, et al.,  “Vocgan: A high-fidelity real-time vocoder with a hierarchically-nested adversarial network,”  arXiv preprint arXiv:2007.15256, 2020.
  * [26] J. Kong, J. Kim, et al.,  “Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis,”  arXiv preprint arXiv:2010.05646, 2020.
  * [27] J. You, D. Kim, et al.,  “Gan vocoder: Multi-resolution discriminator is all you need,”  arXiv preprint arXiv:2103.05236, 2021.
  * [28] I. Gulrajani, F. Ahmed, et al.,  “Improved training of wasserstein gans,”  arXiv preprint arXiv:1704.00028, 2017.
  * [29] X. Mao, Q. Li, et al.,  “Least squares generative adversarial networks,”  in IEEE Int. Conf. on Computer Cision, 2017.
  * [30] I. Goodfellow, J. Pouget-Abadie, et al.,  “Generative adversarial nets,”  Advances in neural information processing systems, vol. 27, 2014\.
  * [31] A. Gretton, K. M Borgwardt, et al.,  “A kernel two-sample test,”  J. of Machine Learning Research, vol. 13, no. 1, 2012.
  * [32] Joseph Turian, Jordie Shier, George Tzanetakis, Kirk McNally, and Max Henry,  “One billion audio sounds from gpu-enabled modular synthesis,”  arXiv preprint arXiv:2104.12922, 2021.
  * [33] J. Cramer, H. Wu, et al.,  “Look, listen, and learn more: Design choices for deep audio embeddings,”  in IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 2019\.
  * [34] D. Moffat and J.D. Reiss,  “Perceptual evaluation of synthesized sound effects,”  ACM Trans. on Applied Perception, vol. 15, no. 2, 2018.
  * [35] N. Jillings, B. De Man, et al.,  “Web audio evaluation tool: A browser-based listening test environment,”  2015\.
  * [36] B. De Man and J.D. Reiss,  “Ape: Audio perceptual evaluation toolbox for matlab,”  in AES Conv. 136, 2014.
  * [37] P. Bahadoran, A. Benito, et al.,  “Fxive: A web platform for procedural sound synthesis,”  in AES Conv. 144, 2018.
  * [38] X. Amatriain, J. Bonada, et al.,  “Spectral processing,”  DAFX-Digital Audio Effects, 2002.
  * [39] J.H. McDermott and E.P. Simoncelli,  “Sound texture perception via statistics of the auditory periphery: evidence from sound synthesis,”  Neuron, vol. 71, no. 5, 2011.
  * [40] C. Verron, M. Aramaki, et al.,  “A 3-d immersive synthesizer for environmental sounds,”  IEEE Trans. on Audio, Speech, and Language Processing, vol. 18, no. 6, 2009.