Improving Singing Voice Separation with the Wave-U-Net Using Minimum Hyperspherical Energy

In recent years, deep learning has surpassed traditional approaches to the problem of singing voice separation. The Wave-U-Net is a recent deep network architecture that operates directly on the time domain. The standard Wave-U- Net is trained with data augmentation and early stopping to prevent overfitting. Minimum hyperspherical energy (MHE) regularization has recently proven to increase generalization in image classification problems by encouraging a diversified filter configuration. In this work, we apply MHE regularization to the 1D filters of the Wave-U-Net. We evaluated this approach for separating the vocal part from mixed music audio recordings on the MUSDB18 dataset. We found that adding MHE regularization to the loss function consistently improves singing voice separation, as measured in the Signal to Distortion Ratio on test recordings, leading to the current best time-domain system for singing voice extraction

Enhancing the Generalization of Convolutional Neural Networks for Speech Emotion Recognition

Human-machine interaction is rapidly gaining significance in our daily lives. While speech recognition has achieved near-human performance in recent years, the intricate details embedded in speech extend beyond the mere arrangement of words. Speech Emotion Recognition (SER) is therefore acquiring a growing role in this field by decoding not only the linguistic content but also the emotional nuances of human spoken communication and enabling therefore a more exhaustive comprehension of the information conveyed by speech signals. Despite the success that neural networks have already achieved in this task, SER is still challenging due to the variability of emotional expression, especially in real-world scenarios where generalization to unseen speakers and contexts is required. In addition, the high resource demand of SER models, combined with the scarcity of emotion-labelled data, hinder the development and application of effective solutions in this field. In this thesis, we present multiple approaches to overcome the aforementioned difficulties. We first introduce a multiple-time-scale (MTS) convolutional neural network architecture to create flexibility towards temporal variations when analyzing time-frequency representations of audio data. We show that resilience to speed fluctuations is relevant in SER tasks, since emotion is expressed through complex spectral patterns that can exhibit significant local dilation and compression on the time axis depending on speaker and context. The results indicate that the use of MTS consistently improves the generalization of networks of different capacity and depth, compared to standard convolution. In a second stage, we propose a more general approach to discourage unwanted sensitivity towards specific target properties in CNNs, introducing the novel concept of anti-transfer learning. While transfer learning assumes that the learning process for a target task will benefit from re-using representations learned for another task, anti-transfer avoids the learning of representations that have been learned for an orthogonal task, i.e., one that is not relevant and potentially confounding for the target task, such as speaker identity and speech content for emotion recognition. In anti-transfer learning we penalize similarity between activations of a network being trained and another network previously trained on an orthogonal task. This leads to better generalization and provides a degree of control over correlations that are spurious or undesirable. We show that anti-transfer actually leads to the intended invariance to the orthogonal task and to more appropriate feature maps for the target task at hand. Anti-transfer creates a computation and memory cost at training time, but it enables enables the reuse of pre-trained models. In order to avoid the high resource demand of SER models in general and anti-transfer learning specifically, we propose RH-emo, a novel semisupervised architecture aimed at extracting quaternion embeddings from realvalued monoaural spectrograms, enabling the use of quaternion-valued networks for SER tasks. RH-emo is a hybrid real/quaternion autoencoder network that consists of a real-valued encoder in parallel to a real-valued emotion classifier and a quaternion-valued decoder. We show that the use of RHemo, combined with quaternion convolutional neural networks provides a consistent improvement in SER tasks, while requiring far fewer trainable parameters and therefore substantially reducing the resource demand of SER models. Finally, we apply anti-transfer learning to quaternion-valued neural networks fed with RH-emo embeddings. We demonstrate that the combination of the two approaches maintains the disentanglement properties of antitransfer, while using a reduced amount of memory, computation, and training time, making this a suitable approach for SER scenarios with limited resources and where context and speaker independence are needed

K-State graduate catalog, 1993-1995

Course catalogs were published under the following titles: Catalogue of the officers and students of the Kansas State Agricultural College, with a brief history of the institution, 1st (1863/4); Annual catalogue of the officers and students of the Kansas State Agricultural College for, 2nd (1864/5)-4th (1868/9); Catalogue of the officers and students of the Kansas State Agricultural College for the year, 1869-1871/2; Hand-book of the Kansas State Agricultural College, Manhattan, Kansas, 1873/4; Biennial catalogue of the Kansas State Agricultural College, Manhattan, Kansas, calendar years, 1875/77; Catalogue of the State Agricultural College of Kansas, 1877/80-1896/97; Annual catalogue of the officers, students and graduates of the Kansas State Agricultural College, Manhattan, 35th (1897/98)-46th (1908/09); Catalogue, 47th (1909/10)-67th (1929/30); Complete catalogue number, 68th (1930/31)-81st (1943/1944); Catalogue, 1945/1946-1948/1949?; General catalogue, 1949/1950?-1958/1960; General catalog, 1960/1962-1990/1992. Course catalogs then split into undergraduate and graduate catalogs respectively: K-State undergraduate catalog, 1992/1994- ; K-State graduate catalog, 1993/1995-Citation: Kansas State University. (1993). K-State graduate catalog, 1993-1995. Manhattan, KS: Kansas State University.Call number: LD2668.A11711 K78

K-State graduate catalog, 1997-1999

Course catalogs were published under the following titles: Catalogue of the officers and students of the Kansas State Agricultural College, with a brief history of the institution, 1st (1863/4); Annual catalogue of the officers and students of the Kansas State Agricultural College for, 2nd (1864/5)-4th (1868/9); Catalogue of the officers and students of the Kansas State Agricultural College for the year, 1869-1871/2; Hand-book of the Kansas State Agricultural College, Manhattan, Kansas, 1873/4; Biennial catalogue of the Kansas State Agricultural College, Manhattan, Kansas, calendar years, 1875/77; Catalogue of the State Agricultural College of Kansas, 1877/80-1896/97; Annual catalogue of the officers, students and graduates of the Kansas State Agricultural College, Manhattan, 35th (1897/98)-46th (1908/09); Catalogue, 47th (1909/10)-67th (1929/30); Complete catalogue number, 68th (1930/31)-81st (1943/1944); Catalogue, 1945/1946-1948/1949?; General catalogue, 1949/1950?-1958/1960; General catalog, 1960/1962-1990/1992. Course catalogs then split into undergraduate and graduate catalogs respectively: K-State undergraduate catalog, 1992/1994- ; K-State graduate catalog, 1993/1995-Citation: Kansas State University. (1997). K-State graduate catalog, 1997-1999. Manhattan, KS: Kansas State University.Call number: LD2668.A11711 K78