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In this thesis, we consider the analysis of music and environmental audio recordings with neural networks. Recently, neural networks have been shown to be an effective family of models for speech recognition, computer vision, natural language processing and a number of other statistical modelling problems. The composite layer-wise structure of neural networks allows for flexible model design, where prior knowledge about the domain of application can be used to inform the design and architecture of the neural network models. Additionally, it has been shown that when trained on sufficient quantities of data, neural networks can be directly applied to low-level features to learn mappings to high level concepts like phonemes in speech and object classes in computer vision. In this thesis we investigate whether neural network models can be usefully applied to processing music and environmental audio. With regards to music signal analysis, we investigate 2 different problems. The fi rst problem, automatic music transcription, aims to identify the score or the sequence of musical notes that comprise an audio recording. We also consider the problem of automatic chord transcription, where the aim is to identify the sequence of chords in a given audio recording. For both problems, we design neural network acoustic models which are applied to low-level time-frequency features in order to detect the presence of notes or chords. Our results demonstrate that the neural network acoustic models perform similarly to state-of-the-art acoustic models, without the need for any feature engineering. The networks are able to learn complex transformations from time-frequency features to the desired outputs, given sufficient amounts of training data. Additionally, we use recurrent neural networks to model the temporal structure of sequences of notes or chords, similar to language modelling in speech. Our results demonstrate that the combination of the acoustic and language model predictions yields improved performance over the acoustic models alone. We also observe that convolutional neural networks yield better performance compared to other neural network architectures for acoustic modelling. For the analysis of environmental audio recordings, we consider the problem of acoustic event detection. Acoustic event detection has a similar structure to automatic music and chord transcription, where the system is required to output the correct sequence of semantic labels along with onset and offset times. We compare the performance of neural network architectures against Gaussian mixture models and support vector machines. In order to account for the fact that such systems are typically deployed on embedded devices, we compare performance as a function of the computational cost of each model. We evaluate the models on 2 large datasets of real-world recordings of baby cries and smoke alarms. Our results demonstrate that the neural networks clearly outperform the other models and they are able to do so without incurring a heavy computation cost.

Music signal analysis is a research field concerning the extraction of meaningful information from musical audio signals. This thesis analyses the music signals from the note-level to the song-level in a bottom-up manner and situates the research in two Music information retrieval (MIR) problems: audio onset detection (AOD) and music structural segmentation (MSS). Most MIR tools are developed for and evaluated on Western music with specific musical knowledge encoded. This thesis approaches the investigated tasks from a cross-cultural perspective by developing audio features and algorithms applicable for both Western and non-Western genres. Two Chinese Jingju databases are collected to facilitate respectively the AOD and MSS tasks investigated. New features and algorithms for AOD are presented relying on fusion techniques. We show that fusion can significantly improve the performance of the constituent baseline AOD algorithms. A large-scale parameter analysis is carried out to identify the relations between system configurations and the musical properties of different music types. Novel audio features are developed to summarise music timbre, harmony and rhythm for its structural description. The new features serve as effective alternatives to commonly used ones, showing comparable performance on existing datasets, and surpass them on the Jingju dataset. A new segmentation algorithm is presented which effectively captures the structural characteristics of Jingju. By evaluating the presented audio features and different segmentation algorithms incorporating different structural principles for the investigated music types, this thesis also identifies the underlying relations between audio features, segmentation methods and music genres in the scenario of music structural analysis.

This thesis analyzes the specific audio signal-music. It describes the basic methods of analysis of musical signals. The following are mentioned the most common music file formats and the possibility of cross transfer. There are explained terms of music theory, which are also present in this work. They are described and created three ways of detecting melody. It is selected optimal algorithm based on the successful detection of the reference melodies recordings. User interface is created in MATLAB GUI allows recognition of recordings. This interface is tested on few melodies.