Download Generative timbre spaces: regularizing variational auto-encoders with perceptual metrics Timbre spaces have been used in music perception to study the perceptual relationships between instruments based on dissimilarity ratings. However, these spaces do not generalize to novel examples and do not provide an invertible mapping, preventing audio synthesis. In parallel, generative models have aimed to provide methods for synthesizing novel timbres. However, these systems do not provide an understanding of their inner workings and are usually not related to any perceptually relevant information. Here, we show that Variational Auto-Encoders (VAE) can alleviate all of these limitations by constructing generative timbre spaces. To do so, we adapt VAEs to learn an audio latent space, while using perceptual ratings from timbre studies to regularize the organization of this space. The resulting space allows us to analyze novel instruments, while being able to synthesize audio from any point of this space. We introduce a specific regularization allowing to enforce any given similarity distances onto these spaces. We show that the resulting space provide almost similar distance relationships as timbre spaces. We evaluate several spectral transforms and show that the Non-Stationary Gabor Transform (NSGT) provides the highest correlation to timbre spaces and the best quality of synthesis. Furthermore, we show that these spaces can generalize to novel instruments and can generate any path between instruments to understand their timbre relationships. As these spaces are continuous, we study how audio descriptors behave along the latent dimensions. We show that even though descriptors have an overall non-linear topology, they follow a locally smooth evolution. Based on this, we introduce a method for descriptor-based synthesis and show that we can control the descriptors of an instrument while keeping its timbre structure.
Download Diet Deep Generative Audio Models With Structured Lottery Deep learning models have provided extremely successful solutions in most audio application fields. However, the high accuracy
of these models comes at the expense of a tremendous computation cost. This aspect is almost always overlooked in evaluating the
quality of proposed models. However, models should not be evaluated without taking into account their complexity. This aspect
is especially critical in audio applications, which heavily relies on
specialized embedded hardware with real-time constraints.
In this paper, we build on recent observations that deep models are highly overparameterized, by studying the lottery ticket hypothesis on deep generative audio models. This hypothesis states
that extremely efficient small sub-networks exist in deep models
and would provide higher accuracy than larger models if trained in
isolation. However, lottery tickets are found by relying on unstructured masking, which means that resulting models do not provide
any gain in either disk size or inference time. Instead, we develop
here a method aimed at performing structured trimming. We show
that this requires to rely on global selection and introduce a specific criterion based on mutual information.
First, we confirm the surprising result that smaller models provide higher accuracy than their large counterparts. We further
show that we can remove up to 95% of the model weights without significant degradation in accuracy. Hence, we can obtain very
light models for generative audio across popular methods such as
Wavenet, SING or DDSP, that are up to 100 times smaller with
commensurate accuracy. We study the theoretical bounds for embedding these models on Raspberry Pi and Arduino, and show that
we can obtain generative models on CPU with equivalent quality
as large GPU models. Finally, we discuss the possibility of implementing deep generative audio models on embedded platforms.