Noise pollution is a major health threat to society. Active noise control systems that attenuate noise through open windows have the potential to create quieter homes while maintaining ventilation and sight. Such systems are commonly realized with closed-loop LMS algorithms. However, these algorithms require a large number of error microphones inside the room and provide only local attenuation. Using many error microphones leads to slow convergence and high computational effort, having additional disadvantages. Therefore, closed-loop active noise control algorithms are undesired for real-world application. In this study, we develop an open-loop wave-domain algorithm that converges instantaneously and operates with low computational effort. As it is open-loop, it does not require error microphones. We position a control region in the far-field that covers all directions from the aperture into the room. In the algorithm, we minimize the sound in that control region. Hence, it inherently ensures cancellation in the whole room. We derive acoustic transfer functions to obtain frequency responses of the aperture and loudspeakers. Those are used for soundfield calculation. The sum of the soundfields, from the aperture and the loudspeaker array, is then expressed in orthonormal basis functions. By minimizing this sum in least mean square sense, we can calculate the filter-weights that minimize the sound energy in the control region. Implementation of these filter-weights with block-wise processing using the Short-Time Fourier Transform generates the signals for the loudspeaker array. However, this processing induces a delay. To compensate for this algorithmic delay, two methods are compared. The first is positioning a reference microphone further in front of the aperture. The second method uses an autoregressive model for signal prediction. Both lead to a loss in attenuation performance compared to the optimal algorithm. We compare the optimal wave-domain algorithm with a LMS-based reference algorithm, as well as both the algorithmic delay compensation methods. The algorithms are tested with a sparse and grid loudspeaker array, and we use rumbler-siren, airplane, and white noise signals as incoming noise. Furthermore, we compare performance for three incident angles. Our simulation results indicate that wave-domain processing has the potential to outperform LMS-based methods in practical active noise control for apertures. More specifically, we obtain an average -10dB global reduction up to 2~kHz for all signal types with the optimal wave-domain approach. In comparison, the performance of the closed-loop algorithm ranges between -5.2 and -9.2dB, depending on the signal type. Furthermore, we indicate the limited impact of the incident angle on performance for the wave-domain algorithm. Positioning a reference microphone in front of the aperture outperforms the predictor approach in all scenarios, and its performance compares to that of the closed-loop LMS algorithms. Eventually, the absence of error microphones and inherent global control ensure that the wave-domain algorithm can be used for a practical active noise control system for apertures. Future work could improve the algorithm by reducing loss of performance with small window-sizes due to time-delay wrapping in the block-wise processing. Furthermore, a natural continuation of this study is to develop and test the wave-domain algorithm for scenarios with a moving primary noise source to further emphasize its advantage over the closed-loop LMS algorithm.

Environmental sound identification and recognition aim to detect sound events within an audio clip. This technology is useful in many real-world applications such as security systems, smart vehicle navigation and surveillance of noise pollution, etc. Research on this topic has received increased attention in recent years. Performance is increasing rapidly as a result of deep learning methods. In this project, our goal is to realize urban sound classification using several neural network models. We select log-Mel spectrogram as the audio representation and use two types of neural networks to perform the classification task. The first is the convolutional neural network (CNN), which is the most straightforward and widely used method for a classification problem. The second type of network is autoencoder based models. This type of model includes the variational autoencoder (VAE), beta-VAE and bounded information rate variational autoencoder (BIR-VAE). The encoders of these systems extract a low dimensionality representation. The classification is then performed on this so-called latent representation. Our experiments assess the performances of different models by evaluation metrics. The results show that CNN is the most promising classifier in our case, autoencoder-based models can successfully reconstruct the log-Mel spectrogram and the latent features learned by encoders are meaningful as classification can be achieved.

Listening in noise is a challenging problem that affects the hearing capability of not only normal hearing but especially hearing impaired people. Since the last four decades, enhancing the quality and intelligibility of noise corrupted speech by reducing the effect of noise has been addressed using statistical signal processing techniques as well as neural networks. However, the fundamental idea behind implementing these methods is the same, i.e., to achieve the best possible estimate of a single target speech waveform. This thesis explores a different route using generative modeling with deep neural networks where speech is artificially generated by conditioning the model on previously predicted samples and features extracted from noisy speech. The proposed system consists of the U-Net model for enhancing the noisy features and the WaveRNN synthesizer (originally proposed for text-to-speech synthesis) re-designed for synthesizing clean sounding speech from noisy features. Subjective results indicate that speech generated by the proposed system is preferred over listening to noisy speech however, the improvement in intelligibility is not significant.