Many real-world time-series analysis problems are characterised by scarce data. Solutions typically rely on hand-crafted features extracted from the time or frequency domain allied with classification or regression engines which condition on this (often low-dimensional) feature vector. The huge advances enjoyed by many application domains in recent years have been fuelled by the use of deep learning architectures trained on large data sets. This paper presents an application of deep learning for acoustic event detection in a challenging, data-scarce, real-world problem. Our candidate challenge is to accurately detect the presence of a mosquito from its acoustic signature. We develop convolutional neural networks (CNNs) operating on wavelet transformations of audio recordings. Furthermore, we interrogate the network's predictive power by visualising statistics of network-excitatory samples. These visualisations offer a deep insight into the relative informativeness of components in the detection problem. We include comparisons with conventional classifiers, conditioned on both hand-tuned and generic features, to stress the strength of automatic deep feature learning. Detection is achieved with performance metrics significantly surpassing those of existing algorithmic methods, as well as marginally exceeding those attained by individual human experts.
The process of automatically detecting an acoustic signal in noise typically consists of an initial preprocessing stage, which involves cleaning and denoising the signal itself, followed by a feature extraction process, in which the signal is transformed into a format suitable for a classifier, followed by the final classification stage. Historically, audio feature extraction in signal processing employed domain knowledge and intricate understanding of digital signal theory @cite_17 , leading to hand-crafted feature representations.
@cite_17
2295991281 2293924895 2071091379 2737712196
Feature extraction is a crucial part of many MIR tasks. In this work, we present a system that can automatically extract relevant features from audio for a given task. The feature extraction system consists of a Deep Belief Network (DBN) on Discrete Fourier Transforms (DFTs) of the audio. We then use the activations of the trained network as inputs for a non-linear Support Vector Machine (SVM) classifier. In particular, we learned the features to solve the task of genre recognition. The learned features perform significantly better than MFCCs. Moreover, we obtain a classification accuracy of 84.3 on the Tzanetakis dataset, which compares favorably against state-of-the-art genre classifiers using frame-based features. We also applied these same features to the task of auto-tagging. The autotaggers trained with our features performed better than those that were trained with timbral and temporal features. In this work we investigate the applicability of unsupervised feature learning methods to the task of automatic genre prediction of music pieces. More specifically we evaluate a framework that recently has been successfully used to recognize objects in images. We first extract local patches from the time-frequency transformed audio signal, which are then pre-processed and used for unsupervised learning of an overcomplete dictionary of local features. For learning we either use a bootstrapped k-means clustering approach or select features randomly. We further extract feature responses in a convolutional manner and train a linear SVM for classification. We extensively evaluate the approach on the GTZAN dataset, emphasizing the influence of important design choices such as dimensionality reduction, pooling and patch dimension on the classification accuracy. We show that convolutional extraction of local feature responses is crucial to reach high performance. Furthermore we find that using this approach, simple and fast learning techniques such as k-means or randomly selected features are competitive with previously published results which also learn features from audio signals. The problem of extracting a signal x n from a noise-corrupted time series y n = x n + e n is considered. The signal x n is assumed to be generated by a discrete-time, deterministic, chaotic dynamical system F, in particular, x n = F n (x 0 ), where the initial point x 0 is assumed to lie in a compact hyperbolic F-invariant set. It is shown that (1) if the noise sequence e n is Gaussian then it is impossible to consistently recover the signal x n , but (2) if the noise sequence consists of i.i.d. random vectors uniformly bounded by a constant δ > 0, then it is possible to recover the signal x n provided δ < 5Δ, where Δ is a separation threshold for F. A filtering algorithm for the latter situation is presented. Urban environments are characterised by the presence of distinctive audio signals which alert the drivers to events that require prompt action. The detection and interpretation of these signals would be highly beneficial for smart vehicle systems, as it would provide them with complementary information to navigate safely in the environment. In this paper, we present a framework that spots the presence of acoustic events, such as horns and sirens, using a two-stage approach. We first model the urban soundscape and use anomaly detection to identify the presence of an anomalous sound, and later determine the nature of this sound. As the audio samples are affected by copious non-stationary and unstructured noise, which can degrade classification performance, we propose a noise-removal technique to obtain a clean representation of the data we can use for classification and waveform reconstruction. The method is based on the idea of analysing the spectrograms of the incoming signals as images and applying spectrogram segmentation to isolate and extract the alerting signals from the background noise. We evaluate our framework on four hours of urban sounds collected driving around urban Oxford on different kinds of road and in different traffic conditions. When compared to traditional feature representations, such as Mel-frequency cepstrum coefficients, our framework shows an improvement of up to 31 in the classification rate.