# Special Issue on Deep Learning for Applications in Acoustics: Modeling, Synthesis, and Listening
## 2 Deep Learning for Applications in Acoustics
Depending on the number of sparse measurement points and spherical harmonic order, however, it can lose high-frequency information, which may cause timbre difference and weaken localization. They attempt to remedy the issue using a combination of convolutional auto-encoder (CAE) and denoising auto-encoder (DAE). The evaluation based on perceptual difference and localization models shows the approach to be promising. Several papers in this special issue deal with music-related tasks such as the generation of bass scores [7], chord progressions [8], and the analysis of rhythmic patterns [9]. [PERSON] et al. [7] present a variational gated auto-encoder model for conditional generation of bass notes. They focus on temporally stable and controllable sequence generation, which is critical in human-collaborative music creation. The temporal stability is secured by the novel use of cross-reconstruction which makes cross predictions between two outputs and two inputs in the nearby position. The controllability is provided by allowing users to take the audio track as input and explore variations from the reference on two dimensional conditional latent space. They also show that the latent space is learned to generate semantically disentangled bass patterns that require a relative pitch representation, and sensitivity to harmony, instrument timbre, and rhythm. In [PERSON] et al. [8] the authors propose an assistive music composition system that is able to satisfy multiple criteria and find multiple candidates in chord sequence generation using an Artificial Immune System, a biologically inspired AI technique based on Genetic Programming for multi-objective optimisation. The generated chord progressions fit a desired tonal tension profile, taking for instance consonance or the melodic attraction of successive chords into account, thus aiding the composition process. The usability of the system as an assistive tool was demonstrated in a small subjective user study. Similarly to harmony in music, rhythmic patters can also be considered in a hierarchy. [PERSON] et al. in [9] puts forward a layered _compositional_ hierarchical model or rhythm, where each successive layer is a composition of one of more parts of the previous layer. The relative spacing and scaling of part relations are modelled by Gaussians, leading to a tempo-invariant rhythmic pattern representation. An unsupervised learning algorithm is proposed for parameter estimation and constructing the model using a greedy optimisation approach. The utilities of the model are demonstrated in rhythm pattern extraction using the Ballroom dataset commonly used in music information retrieval evaluations such as MIREX, and in pieces with varying tempo and time signature. Its robustness to changes make the model applicable to the analysis of a broad range of music genres. Finally, one paper deals with speech processing, more specifically, with the conversion of the timbre of a source speech into that of a target speaker [10]. [PERSON] et al. propose a method based on bidirectional long short-term memory (BLSTM) neural networks for conversion of vocal tracks in presence of noise. A novel architecture with statistical filtering and sub-band cepstrum conversion and fusion is introduced reducing the impact of noise on the input speech. Additionally, a cepstrum filter is proposed to further improve the quality of the converted voice.