Cooperative Scene-Event Modelling for Acoustic Scene Classification

Acoustic scene classification (ASC) can be helpful for creating context awareness for intelligent robots. Humans naturally use the relations between acoustic scenes (AS) and audio events (AE) to understand and recognize their surrounding environments. However, in most previous works, ASC and audio event classification (AEC) are treated as independent tasks, with a focus primarily on audio features shared between scenes and events, but not their implicit relations. To address this limitation, we propose a cooperative scene-event modelling (cSEM) framework to automatically model the intricate scene-event relation by an adaptive coupling matrix to improve ASC. Compared with other scene-event modelling frameworks, the proposed cSEM offers the following advantages. First, it reduces the confusion between similar scenes by aligning the information of coarse-grained AS and fine-grained AE in the latent space, and reducing the redundant information between the AS and AE embeddings. Second, it exploits the relation information between AS and AE to improve ASC, which is shown to be beneficial, even if the information of AE is derived from unverified pseudo-labels. Third, it uses a regression-based loss function for cooperative modelling of scene-event relations, which is shown to be more effective than classification-based loss functions. Instantiated from four models based on either Transformer or convolutional neural networks, cSEM is evaluated on real-life and synthetic datasets. Experiments show that cSEM-based models work well in real-life scene-event analysis, offering competitive results on ASC as compared with other multi-feature or multi-model ensemble methods. The ASC accuracy achieved on the TUT2018, TAU2019, and JSSED datasets is 81.0%, 88.9% and 97.2%, respectively

SLOW-FAST AUDITORY STREAMS FOR AUDIO RECOGNITION

We propose a two-stream convolutional network for audio recognition, that operates on time-frequency spectrogram inputs. Following similar success in visual recognition, we learn Slow-Fast auditory streams with separable convolutions and multi-level lateral connections. The Slow pathway has high channel capacity while the Fast pathway operates at a fine-grained temporal resolution. We showcase the importance of our two-stream proposal on two diverse datasets: VGG-Sound and EPIC-KITCHENS-100, and achieve state-of-the-art results on both.Comment: Accepted for presentation at ICASSP 202

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

ATTENTION-BASED CONVOLUTIONAL NEURAL NETWORK MODEL AND ITS COMBINATION WITH FEW-SHOT LEARNING FOR AUDIO CLASSIFICATION

Environmental sound and acoustic scene classification are crucial tasks in audio signal processing and audio pattern recognition. In recent years, deep learning methods such as convolutional neural networks (CNN), recurrent neural networks (RNN), and their com- binations, have achieved great success in such tasks. However, there are still numerous challenges left to be addressed in this domain. For example, in most cases, the sound events of interest will be present through only a portion of the entire audio clip, and the clip can also suffer from the background noise. Furthermore, in many application scenarios where the amount of labelled training data can be very limited, the application of few- shot learning methods especially prototypical networks have achieved great success. But metric learning methods such as prototypical networks often suffer from bad feature em- beddings of support samples or outliers, or may not perform well on noisy data. Therefore, the proposed work seeks to overcome the above limitations by introducing a multi-channel temporal attention-based CNN model and then introduce a hybrid attention module into the framework of prototypical networks. Additionally, a Π-model is integrated into our model to improve performance on noisy data, and a new time-frequency feature is explored. Var- ious experiments have shown that our proposed framework is capable of dealing with the above mentioned issues and providing promising results.Ph.D

Sound Classification and Processing of Urban Environments: A Systematic Literature Review

Audio recognition can be used in smart cities for security, surveillance, manufacturing, autonomous vehicles, and noise mitigation, just to name a few. However, urban sounds are everyday audio events that occur daily, presenting unstructured characteristics containing different genres of noise and sounds unrelated to the sound event under study, making it a challenging problem. Therefore, the main objective of this literature review is to summarize the most recent works on this subject to understand the current approaches and identify their limitations. Based on the reviewed articles, it can be realized that Deep Learning (DL) architectures, attention mechanisms, data augmentation techniques, and pretraining are the most crucial factors to consider while creating an efficient sound classification model. The best-found results were obtained by Mushtaq and Su, in 2020, using a DenseNet-161 with pretrained weights from ImageNet, and NA-1 and NA-2 as augmentation techniques, which were of 97.98%, 98.52%, and 99.22% for UrbanSound8K, ESC-50, and ESC-10 datasets, respectively. Nonetheless, the use of these models in real-world scenarios has not been properly addressed, so their effectiveness is still questionable in such situations

A Geometric Deep Learning Approach to Sound Source Localization and Tracking

La localización y el tracking de fuentes sonoras mediante agrupaciones de micrófonos es un problema que, pese a llevar décadas siendo estudiado, permanece abierto. En los últimos años, modelos basados en deep learning han superado el estado del arte que había sido establecido por las técnicas clásicas de procesado de señal, pero estos modelos todavía presentan problemas para trabajar en espacios con alta reverberación o para realizar el tracking de varias fuentes sonoras, especialmente cuando no es posible aplicar ningún criterio para clasificarlas u ordenarlas. En esta tesis, se proponen nuevos modelos que, basados en las ideas del Geometric Deep Learning, suponen un avance en el estado del arte para las situaciones mencionadas previamente.Los modelos propuestos utilizan como entrada mapas de potencia acústica calculados con el algoritmo SRP-PHAT, una técnica clásica de procesado de señal que permite estimar la energía acústica recibida desde cualquier dirección del espacio. Además, también proponemos una nueva técnica para suprimir analíticamente el efecto de una fuente en las funciones de correlación cruzada usadas para calcular los mapas SRP-PHAT. Basándonos en técnicas de banda estrecha, se demuestra que es posible proyectar las funciones de correlación cruzada de las señales capturadas por una agrupación de micrófonos a un espacio ortogonal a una dirección dada simplemente usando una combinación lineal de las funciones originales con retardos temporales. La técnica propuesta puede usarse para diseñar sistemas iterativos de localización de múltiples fuentes que, tras localizar la fuente con mayor energía en las funciones de correlación cruzada o en los mapas SRP-PHAT, la cancelen para poder encontrar otras fuentes que estuvieran enmascaradas por ella.Antes de poder entrenar modelos de deep learning necesitamos datos. Esto, en el caso de seguir un esquema de aprendizaje supervisado, supone un dataset de grabaciones de audio multicanal con la posición de las fuentes etiquetada con precisión. Pese a que existen algunos datasets con estas características, estos no son lo suficientemente extensos para entrenar una red neuronal y los entornos acústicos que incluyen no son suficientemente variados. Para solventar el problema de la falta de datos, presentamos una técnica para simular escenas acústicas con una o varias fuentes en movimiento y, para realizar estas simulaciones conforme son necesarias durante el entrenamiento de la red, presentamos la que es, que sepamos, la primera librería de software libre para la simulación de acústica de salas con aceleración por GPU. Tal y como queda demostrado en esta tesis, esta librería es más de dos órdenes de magnitud más rápida que otras librerías del estado del arte.La idea principal del Geometric Deep Learning es que los modelos deberían compartir las simetrías (i.e. las invarianzas y equivarianzas) de los datos y el problema que se quiere resolver. Para la estimación de la dirección de llegada de una única fuente, el uso de mapas SRP-PHAT como entrada de nuestros modelos hace que la equivarianza a las rotaciones sea obvia y, tras presentar una primera aproximación usando redes convolucionales tridimensionales, presentamos un modelo basado en convoluciones icosaédricas que son capaces de aproximar la equivarianza al grupo continuo de rotaciones esféricas por la equivarianza al grupo discreto de las 60 simetrías del icosaedro. En la tesis se demuestra que los mapas SRP-PHAT son una característica de entrada mucho más robusta que los espectrogramas que se usan típicamente en muchos modelos del estado del arte y que el uso de las convoluciones icosaédricas, combinado con una nueva función softargmax que obtiene una salida de regresión a partir del resultado de una red convolucional interpretándolo como una distribución de probabilidad y calculando su valor esperado, permite reducir enormemente el número de parámetros entrenables de los modelos sin reducir la precisión de sus estimaciones.Cuando queremos realizar el tracking de varias fuentes en movimiento y no podemos aplicar ningún criterio para ordenarlas o clasificarlas, el problema se vuelve invariante a las permutaciones de las estimaciones, por lo que no podemos compararlas directamente con las etiquetas de referencia dado que no podemos esperar que sigan el mismo orden. Este tipo de modelos se han entrenado típicamente usando estrategias de entrenamiento invariantes a las permutaciones, pero estas normalmente no penalizan los cambios de identidad por lo que los modelos entrenados con ellas no mantienen la identidad de cada fuente de forma consistente. Para resolver este problema, en esta tesis proponemos una nueva estrategia de entrenamiento, a la que llamamos sliding permutation invariant training (sPIT), que es capaz de optimizar todas las características que podemos esperar de un sistema de tracking de múltiples fuentes: la precisión de sus estimaciones de dirección de llegada, la exactitud de sus detecciones y la consistencia de las identidades asignadas a cada fuente.Finalmente, proponemos un nuevo tipo de red recursiva que usa conjuntos de vectores en lugar de vectores para representar su entrada y su estado y que es invariante a las permutaciones de los elementos del conjunto de entrada y equivariante a las del conjunto de estado. En esta tesis se muestra como este es el comportamiento que deberíamos esperar de un sistema de tracking que toma como entradas las estimaciones de un modelo de localización multifuente y se compara el rendimiento de estas redes recursivas invariantes a las permutaciones con redes recursivas GRU convencionales para aplicaciones de tracking de fuentes sonoras.The localization and tracking of sound sources using microphone arrays is a problem that, even if it has attracted attention from the signal processing research community for decades, remains open. In recent years, deep learning models have surpassed the state-of-the-art that had been established by classic signal processing techniques, but these models still struggle with handling rooms with strong reverberations or tracking multiple sources that dynamically appear and disappear, especially when we cannot apply any criteria to classify or order them. In this thesis, we follow the ideas of the Geometric Deep Learning framework to propose new models and techniques that mean an advance of the state-of-the-art in the aforementioned scenarios. As the input of our models, we use acoustic power maps computed using the SRP-PHAT algorithm, a classic signal processing technique that allows us to estimate the acoustic energy received from any direction of the space and, therefore, compute arbitrary-shaped power maps. In addition, we also propose a new technique to analytically cancel a source from the generalized cross-correlations used to compute the SRP-PHAT maps. Based on previous narrowband cancellation techniques, we prove that we can project the cross-correlation functions of the signals captured by a microphone array into a space orthogonal to a given direction by just computing a linear combination of time-shifted versions of the original cross-correlations. The proposed cancellation technique can be used to design iterative multi-source localization systems where, after having found the strongest source in the generalized cross-correlation functions or in the SRP-PHAT maps, we can cancel it and find new sources that were previously masked by thefirst source. Before being able to train deep learning models we need data, which, in the case of following a supervised learning approach, means a dataset of multichannel recordings with the position of the sources accurately labeled. Although there exist some datasets like this, they are not large enough to train a neural network and the acoustic environments they include are not diverse enough. To overcome this lack of real data, we present a technique to simulate acoustic scenes with one or several moving sound sources and, to be able to perform these simulations as they are needed during the training, we present what is, to the best of our knowledge, the first free and open source room acoustics simulation library with GPU acceleration. As we prove in this thesis, the presented library is more than two orders of magnitude faster than other state-of-the-art CPU libraries. The main idea of the Geometric Deep Learning philosophy is that the models should fit the symmetries (i.e. the invariances and equivariances) of the data and the problem we want to solve. For single-source direction of arrival estimation, the use of SRP-PHAT maps as inputs of our models makes the rotational equivariance of the problem undeniably clear and, after a first approach using 3D convolutional neural networks, we present a model using icosahedral convolutions that approximate the equivariance to the continuous group of spherical rotations by the discrete group of the 60 icosahedral symmetries. We prove that the SRP-PHAT maps are a much more robust input feature than the spectrograms typically used in many state-of-the-art models and that the use of the icosahedral convolutions, combined with a new soft-argmax function that obtains a regression output from the output of the convolutional neural network by interpreting it as a probability distribution and computing its expected value, allows us to dramatically reduce the number of trainable parameters of the models without losing accuracy in their estimations. When we want to track multiple moving sources and we cannot use any criteria to order or classify them, the problem becomes invariant to the permutations of the estimates, so we cannot directly compare them with the ground truth labels since we cannot expect them to be in the same order. This kind of models has typically been trained using permutation invariant training strategies, but these strategies usually do not penalize the identity switches and the models trained with them do not keep the identity of every source consistent during the tracking. To solve this issue, we propose a new training strategy, which we call sliding permutation invariant training, that is able to optimize all the features that we could expect from a multi-source tracking system: the precision of the direction of arrival estimates, the accuracy of the source detections, and the consistency of the assigned identities. Finally, we propose a new kind of recursive neural network that, instead of using vectors as their input and their state, uses sets of vectors and is invariant to the permutation of the elements of the input set and equivariant to the permutations of the elements of the state set. We show how this is the behavior that we should expect from a tracking model which takes as inputs the estimates of a multi-source localization model and compare these permutation-invariant recursive neural networks with the conventional gated recurrent units for sound source tracking applications.<br /