# Special Issue on Deep Learning for Applications in Acoustics: Modeling, Synthesis, and Listening
## 2 Deep Learning for Applications in Acoustics  
The topics covered by the published papers cover sound synthesis, generative music, spatial audio, bioacoustics, audio scene classification and more. We would like to start our survey by highlighting those topics that are rarely addressed by the audio and acoustics communities, but pose new interesting challenges. The paper from [PERSON] and [PERSON] [1] addresses the need for generating heart sound datasets for training neural networks. The goal is to expand the currently limited datasets with synthesized sounds that can be classified by neural networks as normal, as opposed to abnormal. In this work, Generative Adversarial Networks (GANs) are employed to generate heart sounds and the Empirical Wavelet Transform (EWT) isemployed to clean the generated waveforms from noise, thus reducing the training time. A quantitative assessment is provided using a mel-cepstral distortion metric, and the results are compared to another method present in the literature with a positive outcome. Additionally, several heart sound classification methods have been compared with the generated dataset. The paper from [PERSON] et al. [2] deals with the classification of vocalization spectrograms of Antillean Manatee, a large endangered sea mammal. In order to estimate its population for scientific purposes, the authors turn to audio classification. Their previous studies successfully managed to identify and count these mammals in the area of Panama by analyzing audio recordings with traditional DSP techniques. In this work, they aim to improve the robustness of the classification by using Deep Learning methods. They explore the use of different types of Convolutional Network architectures and different input features. The authors also report a set of experiments aimed at understanding the role of the dataset preparation, the importance of cross-validation, discussing the trained features of the network and comparing to a method based on traditional FFT processing. We turn now our attention to more traditional audio applications, such as Acoustic Scene Classification and filter design. A review of Deep Learning techniques for Acoustic Scene Classification is provided by [PERSON] [3]. This field has gathered attention in the last years, and of particular importance is the annual Detection and Classification of Acoustic Scenese and Events (DCASE) competition. This timely review by [PERSON] outlines about a hundred papers in the field, providing a perspective of current research trends. In [PERSON] et al. [4], multichannel equalization is investigated with a novel approach based on nonlinear optimization, were Neural Networks are employed to generate the FIR coefficients that minimize a target function based on spectral flatness. The results exceed traditional methods including Frequency Deconvolution, obtaining extremely flat frequency responses at multiple positions in a car environment with several loudspeakers. Spatial audio is covered by two papers with interesting results. In [5], [PERSON] and colleagues compare the performance of humans and machines in detecting a spatial audio scene. Specifically, the task is to assess the position of a music ensemble that is located in front of the listener, to the back or around her/him. Human subjects undertook the test remotely over the Internet, therefore with unknown listening devices. Their accuracy is relatively low. Several Deep Learning algorithms are tested, showing very high classification results under known conditions, however scoring similarly to humans when the electro-acoustic conditions are unknown to the machine. This opens new interesting challenges for Machine Listening models. In [6], [PERSON] et al. investigate the restoration of high-frequency distortions in spherical harmonic interpolation using neural network models. Spherical harmonic interpolation is an effective method to upsample a sparse head-related transfer function (HRTF) measurement to a dense set of HRTFs.