Learning weakly supervised multimodal phoneme embeddings

Recent works have explored deep architectures for learning multimodal speech representation (e.g. audio and images, articulation and audio) in a supervised way. Here we investigate the role of combining different speech modalities, i.e. audio and visual information representing the lips movements, in a weakly supervised way using Siamese networks and lexical same-different side information. In particular, we ask whether one modality can benefit from the other to provide a richer representation for phone recognition in a weakly supervised setting. We introduce mono-task and multi-task methods for merging speech and visual modalities for phone recognition. The mono-task learning consists in applying a Siamese network on the concatenation of the two modalities, while the multi-task learning receives several different combinations of modalities at train time. We show that multi-task learning enhances discriminability for visual and multimodal inputs while minimally impacting auditory inputs. Furthermore, we present a qualitative analysis of the obtained phone embeddings, and show that cross-modal visual input can improve the discriminability of phonological features which are visually discernable (rounding, open/close, labial place of articulation), resulting in representations that are closer to abstract linguistic features than those based on audio only

Audio Deepfake Detection: A Survey

Audio deepfake detection is an emerging active topic. A growing number of literatures have aimed to study deepfake detection algorithms and achieved effective performance, the problem of which is far from being solved. Although there are some review literatures, there has been no comprehensive survey that provides researchers with a systematic overview of these developments with a unified evaluation. Accordingly, in this survey paper, we first highlight the key differences across various types of deepfake audio, then outline and analyse competitions, datasets, features, classifications, and evaluation of state-of-the-art approaches. For each aspect, the basic techniques, advanced developments and major challenges are discussed. In addition, we perform a unified comparison of representative features and classifiers on ASVspoof 2021, ADD 2023 and In-the-Wild datasets for audio deepfake detection, respectively. The survey shows that future research should address the lack of large scale datasets in the wild, poor generalization of existing detection methods to unknown fake attacks, as well as interpretability of detection results

Deep Learning for Raman Spectroscopy: Bridging the Gap between Experimental Data and Molecular Analysis

Raman spectroscopy is an analytical method frequently used in the fields of materials science and general chemistry, which measures the characteristic responses of molecules to light. Being a non-destructive technique, it has many scientific and industrial applications, such as material identification, drug production and airport security. By combining Raman spectroscopy with machine learning, powerful tools can be developed to make accurate predictions on unknown substances or quantities, without explicit programming. This thesis focuses on two main areas. Firstly, through the application of machine learning and image processing, a novel tool was developed to study single molecule interactions with metal surfaces using surface-enhanced Raman spectroscopy (SERS). A convolutional autoencoder (CAE) architecture was utilised in a processing pipeline alongside various image processing techniques to extract and isolate complex, transient Raman features from SERS data. This was followed by a clustering process to obtain representative events pertaining to atomic-scale metal-molecule interactions on multiple catalyst surfaces, which provided a unique insight into the formation dynamics of atomic-scale features. The process was extended through the use of a Siamese convolutional neural network (Siamese-CNN) to incorporate spatiotemporal information relating to interactions between individual vibrational modes. This foundational research paves the way for tailoring metal-molecule interactions and assists in rational heterogeneous catalyst design. It introduces an analytical tool capable of studying metal-molecule interactions under the influence of strong local field gradients. This is a scenario that cannot be efficiently modelled with the conventional quantum mechanical method, density functional theory (DFT), which assumes a homogeneous field when analysing electronic structures of molecules. Secondly, machine learning analysis has been applied to Raman data obtained in both nuclear and biopharmaceutical industrial applications. A key focus of this work is on the practical challenges faced in the design of data processing tasks and machine learning architectures due to real-world limitations in data collection. A fully connected (FC) autoencoder is employed as part of a regression task, which generates predictions on analyte concentrations in mixed substances. The method was shown to outperform industry standard regression tools, principal component regression (PCR) and partial least squares (PLS) regression, each used as comparative benchmarks, by over 50% in a test of model precision across various datasets in the investigated industrial applications. Advancements in the precision, speed and effectiveness of such tools are of critical importance in an industrial environment. This is driven by compelling motivations to reduce not only the costs associated with these procedures, but also to increase the quality of resulting products, or to reduce the risks within industrial operations, where applicable

Classification of Sound Scenes and Events in Real-World Scenarios with Deep Learning Techniques

La clasificación de los eventos sonoros es un campo de la audición por computador que se está volviendo cada vez más interesante debido al gran número de aplicaciones que podrían beneficiarse de esta tecnología. A diferencia de otros campos de la audición por computador relacionados con la recuperación de información musical o el reconocimiento del habla, la clasificación de eventos sonoros tiene una serie de problemas intrínsecos. Estos problemas son la naturaleza polifónica de la mayoría de las grabaciones de sonido ambiental, la diferencia en la naturaleza de cada sonido, la falta de estructura temporal y la adición de ruido de fondo y reverberación en el proceso de grabación. Estos problemas son campos de estudio para la comunidad científica a día de hoy. Sin embargo, cabe señalar que cuando se despliega una solución de audición por computador en entornos reales, pueden surgir una serie de problemas adicionales. Estos problemas son el Reconocimiento de Conjunto Abierto (OSR), el Aprendizaje de Pocos Disparos (FSL) y la consideración del tiempo de ejecución del sistema (baja complejidad). El OSR se define como el problema que aparece cuando un sistema de inteligencia artificial tiene que enfrentarse a una situación desconocida en la que clases no vistas durante la etapa de entrenamiento están presentes en una etapa de inferencia. El FSL corresponde al problema que se produce cuando hay muy pocas muestras disponibles para cada clase considerada. Por último, dado que estos sistemas se despliegan normalmente en dispositivos de borde, hay que tener en cuenta el tiempo de ejecución, ya que cuanto menos tiempo tarde el sistema en dar una respuesta, mejor será la experiencia percibida por los usuarios. Las soluciones basadas en las técnicas de aprendizaje en profundidad para problemas similares en el dominio de la imagen han mostrado resultados prometedores. Las soluciones más difundidas son las que implementan Redes Neuronales Convolucionales (CNN). Por lo tanto, muchos sistemas de audio de última generación proponen convertir las señales de audio en una representación bidimensional que puede ser tratada como una imagen. La generación de mapas internos se realiza a menudo por las capas convolucionales de las CNN. Sin embargo, estas capas tienen una serie de limitaciones que deben ser estudiadas para poder proponer técnicas para mejorar los mapas de características resultantes. Con este fin, se han propuesto novedosas redes que fusionan dos métodos diferentes, como el aprendizaje residual y las técnicas de excitación y compresión. Los resultados muestran una mejora de la precisión del sistema con la adición de un número reducido de parámetros adicionales. Por otra parte, estas soluciones basadas en entradas bidimensionales pueden mostrar un cierto sesgo, ya que la elección de la representación de audio puede ser específica para una tarea concreta. Por lo tanto, se ha realizado un estudio comparativo de diferentes redes residuales alimentadas directamente por la señal de audio en bruto. Estas soluciones se conocen como de extremo a extremo. Si bien se han realizado estudios similares en la literatura en el dominio de la imagen, los resultados sugieren que los bloques residuales de mejor rendimiento para las tareas de visión artificial pueden no ser los mismos que los de la clasificación de audio. En cuanto a los problemas de FSL y OSR, se propone un marco basado en un autoencoder capaz de mitigar ambos problemas juntos. Esta solución es capaz de crear representaciones robustas de estos patrones de audio a partir de sólo unas pocas muestras, al tiempo que es capaz de rechazar las clases de audio no deseadas.The classification of sound events is a field of machine listening that is becoming increasingly interesting due to the large number of applications that could benefit from this technology. Unlike other fields of machine listening related to music information retrieval or speech recognition, sound event classification has a number of intrinsic problems. These problems are the polyphonic nature of most environmental sound recordings, the difference in the nature of each sound, the lack of temporal structure and the addition of background noise and reverberation in the recording process. These problems are fields of study for the scientific community today. However, it should be noted that when a machine listening solution is deployed in real environments, a number of extra problems may arise. These problems are Open-Set Recognition (OSR), Few-Shot Learning (FSL) and consideration of system runtime (low-complexity). OSR is defined as the problem that appears when an artificial intelligence system has to face an unknown situation where classes unseen during the training stage are present at a usage stage. FSL corresponds to the problem that occurs when there are very few samples available for each considered class. Finally, since these systems are normally deployed in edge devices, the consideration of the execution time must be taken into account, as the less time the system takes to give a response, the better the experience perceived by the users. Solutions based on Deep Learning techniques for similar problems in the image domain have shown promising results. The most widespread solutions are those that implement Convolutional Neural Networks (CNNs). Therefore, many state-of-the-art audio systems propose to convert audio signals into a two-dimensional representation that can be treated as an image. The generation of internal maps is often done by the convolutional layers of the CNNs. However, these layers have a series of limitations that must be studied in order to be able to propose techniques for improving the resulting feature maps. To this end, novel networks have been proposed that merge two different methods such as residual learning and squeeze-excitation techniques. The results show an improvement in the accuracy of the system with the addition of few number of extra parameters. On the other hand, these solutions based on two-dimensional inputs can show a certain bias since the choice of audio representation can be specific to a particular task. Therefore, a comparative study of different residual networks directly fed by the raw audio signal has been carried out. These solutions are known as end-to-end. While similar studies have been carried out in the literature in the image domain, the results suggest that the best performing residual blocks for computer vision tasks may not be the same as those for audio classification. Regarding the FSL and OSR problems, an autoencoder-based framework capable of mitigating both problems together is proposed. This solution is capable of creating robust representations of these audio patterns from just a few samples while being able to reject unwanted audio classes