Continuously moving microphones produce a high number of spatially dense sound-field samples with low effort in hardware and acquisition time. By interpreting the dynamic procedure as the non-uniform sampling of spatial basis functions, a system of linear equations can be set up. Its solution encodes sound-field parameters that allow for the spatio-temporal reconstruction within the measurement area at bandwidths where static methods would require impractical setups. An existing framework considers such basis functions from a signal processing point of view. It uses sinc-function based interpolation filters which are highly localized around sampled trajectories and may lead to ill-posed problems unless sparsity constraints are made, especially for locations that are away from microphone trajectories. In this paper, we present a new physical interpretation of the dynamic sampling problem. Transferring the problem into frequency domain, we describe samples of a moving microphone in terms of sampled spherical harmonic functions. The use of these global basis functions leads to dynamic measurements that inherently encode expanded sound-field information and, thus, allow for robust reconstruction at off-trajectory positions.

In this article, a new convex formulation for the acoustic channel-equalization problem is proposed and efficient ways for solving it are presented. Both the alternating direction method of multipliers and a proximal algorithm are studied for optimization. Time-domain and frequency-domain constraints are included in an equal way, allowing for simultaneous reverberation reduction and frequency-response equalization. Rather than aiming at full channel equalization, we follow the channel-reshaping paradigm proposed in earlier works and try to push the reverberation tail under the temporal masking limit of the human auditory system, thus making reverberation inaudible for human listeners. While the algorithm is presented for the multiple-input-multiple-output (MIMO) scenario, the single-channel case is included as a special case. Comparisons with methods from the literature for the single- and multi-channel cases show that the new algorithm has faster convergence than the known ones. The proximal algorithm even allows for solving extremely large problems with very low memory demand.

For many applications of hand gesture recognition, a delay-free, affordable, and mobile system relying on body signals is mandatory. Therefore, we propose an approach for hand gestures classification given signals of inertial measurement units (IMUs) that works with extremely short windows to avoid delays. With a simple recurrent neural network the suitability of the sensor modalities of an IMU (accelerometer, gyroscope, magnetometer) are evaluated by only providing data of one modality. For the multi-modal data a second network with mid-level fusion is proposed. Its forked architecture allows us to process data of each modality individually before carrying out a joint analysis for classification. Experiments on three databases reveal that even when relying on a single modality our proposed system outperforms state-of-the-art systems significantly. With the forked network classification accuracy can be further improved by over 10 % absolute compared to the best reported system while causing a fraction of the delay.

For conventional sampling of sound-fields, the measurement in space by use of stationary microphones is impractical for high audio frequencies. Satisfying the Nyquist-Shannon sampling theorem requires a huge number of sampling points and entails other difficulties, such as the need for exact calibration and spatial positioning of a large number of microphones. Dynamic sound-field measurements involving tracked microphones may weaken this spatial sampling problem. However, for aliasing-free reconstruction, there is still the need of sampling a huge number of unknown sound-field variables. Thus in real-world applications, the trajectories may be expected to lead to underdetermined sampling problems. In this paper, we present a compressed sensing framework that allows for stable and robust sub-Nyquist sampling of sound fields by use of moving microphones.

The conventional sampling of sound fields by use of stationary microphones is impractical for large bandwidths. Satisfying the Nyquist-Shannon sampling theorem in three-dimensional space requires a huge number of sampling positions. Dynamic sound-field measurements with moving microphones together with a compressed-sensing recovery allow for weakening the spatial sampling problem. For bandlimited signals, the dynamic samples taken along the microphone trajectory may be related to the room impulse responses on a virtual grid in space via spatial interpolation. The tracking of the microphone positions and the knowledge of the excitation sequence allow for setting up a linear system of equations that can be solved for the room impulse responses on the modeled virtual grid. Nevertheless, there is still the necessity for recovering a huge number of sound-field variables, in order to ensure aliasing-free reconstruction. Thus, for practical applications, random or suboptimally chosen trajectories may be expected to lead to underdetermined sampling problems for a given volume of interest. In this paper, we present a compressed sensing framework that enables us to uniquely solve the dynamic sampling problem despite having underdetermined variables. The spatio-temporal sampling problem is integrated into compressed sensing models that allow for stable and robust sub-Nyquist sampling given incoherent measurements. For a modeled equidistant grid and sparse Fourier representations, the influence of the microphone trajectories on the compressed sensing problem is investigated and a simple expression is derived for evaluating trajectories with regard to compressed-sensing based recovery.

The sampling of sound fields involves the measurement of spatially dependent room impulse responses, where the Nyquist-Shannon sampling theorem applies in both the temporal and spatial domains. Therefore, sampling inside a volume of interest requires a huge number of sampling points in space, which comes along with further difficulties such as exact microphone positioning and calibration of multiple microphones. In this paper, we present a method for measuring sound fields using moving microphones whose trajectories are known to the algorithm. At that, the number of microphones is customizable by trading measurement effort against sampling time. Through spatial interpolation of the dynamic measurements, a system of linear equations is set up which allows for the reconstruction of the entire sound field inside the volume of interest.

We present in this work an approach for audio scene classification. Firstly, given the label set of the scenes, a label tree is automatically constructed where the labels are grouped into meta-classes. This category taxonomy is then used in the feature extraction step in which an audio scene instance is transformed into a label tree embedding image. Elements of the image indicate the likelihoods that the scene instances belong to different meta-classes. A class of simple 1-X (i.e. 1-max, 1-mean, and 1-mix) pooling convolutional neural networks, which are tailored for the task at hand, are finally learned on top of the image features for scene recognition. Experimental results on the DCASE 2013 and DCASE 2016 datasets demonstrate the efficiency of the proposed method.

We introduce in this work an efficient approach for audio scene classification using deep recurrent neural networks. An audio scene is firstly transformed into a sequence of high-level label tree embedding feature vectors. The vector sequence is then divided into multiple subsequences on which a deep GRU-based recurrent neural network is trained for sequence-to-label classification. The global predicted label for the entire sequence is finally obtained via aggregation of subsequence classification outputs. We will show that our approach obtains an F1-score of 97.7% on the LITIS Rouen dataset, which is the largest dataset publicly available for the task. Compared to the best previously reported result on the dataset, our approach is able to reduce the relative classification error by 35.3%.

In this paper, we present an efficient approach for audio scene classification. We aim at learning representations for scene examples by exploring the structure of their class labels. A category taxonomy is automatically learned by collectively optimizing a tree-structured clustering of the given labels into multiple metaclasses. A scene recording is then transformed into a label-tree embedding image. Elements of the image represent the likelihoods that the scene instance belongs to the metaclasses. We investigate classification with label-tree embedding features learned from different low-level features as well as their fusion. We show that the combination of multiple features is essential to obtain good performance. While averaging label-tree embedding images over time yields good performance, we argue that average pooling possesses an intrinsic shortcoming. We alternatively propose an improved classification scheme to bypass this limitation. We aim at automatically learning common templates that are useful for the classification task from these images using simple but tailored convolutional neural networks. The trained networks are then employed as a feature extractor that matches the learned templates across a label-tree embedding image and produce the maximum matching scores as features for classification. Since audio scenes exhibit rich content, template learning and matching on low-level features would be inefficient. With label-tree embedding features, we have quantized and reduced the low-level features into the likelihoods of the metaclasses, on which the template learning and matching are efficient. We study both training convolutional neural networks on stacked label-tree embedding images and multistream networks. Experimental results on the DCASE2016 and LITIS Rouen datasets demonstrate the efficiency of the proposed methods.

We introduce a new learned descriptor for audio signals which is efficient for event representation. The entries of the descriptor are produced by evaluating a set of regressors on the input signal. The regressors are class-specific and trained using the random regression forests framework. Given an input signal, each regressor estimates the onset and offset positions of the target event. The estimation confidence scores output by a regressor are then used to quantify how the target event aligns with the temporal structure of the corresponding category. Our proposed descriptor has two advantages. First, it is compact, i.e. the dimensionality of the descriptor is equal to the number of event classes. Second, we show that even simple linear classification models, trained on our descriptor, yield better accuracies on audio event classification task than not only the nonlinear baselines but also the state-of-the-art results.

We present in this paper a simple, yet efficient convolutional neural network (CNN) architecture for robust audio event recognition. Opposing to deep CNN architectures with multiple convolutional and pooling layers topped up with multiple fully connected layers, the proposed network consists of only three layers: convolutional, pooling, and softmax layer. Two further features distinguish it from the deep architectures that have been proposed for the task: varying-size convolutional filters at the convolutional layer and 1-max pooling scheme at the pooling layer. In intuition, the network tends to select the most discriminative features from the whole audio signals for recognition. Our proposed CNN not only shows state-of-the-art performance on the standard task of robust audio event recognition but also outperforms other deep architectures up to 4.5% in terms of recognition accuracy, which is equivalent to 76.3% relative error reduction.

We present in this paper an efficient approach for acoustic scene classification by exploring the structure of class labels. Given a set of class labels, a category taxonomy is automatically learned by collectively optimizing a clustering of the labels into multiple meta-classes in a tree structure. An acoustic scene instance is then embedded into a low-dimensional feature representation which consists of the likelihoods that it belongs to the meta-classes. We demonstrate state-of-the-art results on two different datasets for the acoustic scene classification task, including the DCASE 2013 and LITIS Rouen datasets.

The human auditory system is very well matched to both human speech and environmental sounds. Therefore, the question arises whether human speech material may provide useful information for training systems for analyzing nonspeech audio signals, e.g., in a classification task. In order to answer this question, we consider speech patterns as basic acoustic concepts, which embody and represent the target nonspeech signal. To find out how similar the nonspeech signal is to speech, we classify it with a classifier trained on the speech patterns and use the classification posteriors to represent the closeness to the speech bases. The speech similarities are finally employed as a descriptor to represent the target signal. We further show that a better descriptor can be obtained by learning to organize the speech categories hierarchically with a tree structure. Furthermore, these descriptors are generic. That is, once the speech classifier has been learned, it can be employed as a feature extractor for different datasets without retraining. Lastly, we propose an algorithm to select a sufficient subset, which provides an approximate representation capability of the entire set of available speech patterns. We conduct experiments for the application of audio event analysis. Phone triplets from the TIMIT dataset were used as speech patterns to learn the descriptors for audio events of three different datasets with different complexity, including UPC-TALP, Freiburg-106, and NAR. The experimental results on the event classification task show that a good performance can be easily obtained even if a simple linear classifier is used. Furthermore, fusion of the learned descriptors as an additional source leads to state-of-the-art performance on all the three target datasets.

Audio event detection has been an active field of research in recent years. However, most of the proposed methods, if not all, analyze and detect complete events and little attention has been paid for early detection. In this paper, we present a system which enables early audio event detection in continuous audio recordings in which an event can be reliably recognized when only a partial duration is observed. Our evaluation on the ITC-Irst database, one of the standard database of the CLEAR 2006 evaluation, shows that: on one hand, the proposed system outperforms the best baseline system by 16% and 8% in terms of detection error rate and detection accuracy respectively; on the other hand, even partial events are enough to achieve the performance that is obtainable when the whole events are observed.

The human auditory system is very well matched to both human speech and environmental sounds. Therefore, the question arises whether human speech material may provide useful information for training systems for analyzing nonspeech audio signals, such as in a recognition task. To find out how similar nonspeech signals are to speech, we measure the closeness between target nonspeech signals and different basis speech categories via a speech classification model. The speech similarities are finally employed as a descriptor to represent the target signal. We further show that a better descriptor can be obtained by learning to organize the speech categories hierarchically with a tree structure. We conduct experiments for the audio event analysis application by using speech words from the TIMIT dataset to learn the descriptors for the audio events of the Freiburg-106 dataset. Our results on the event recognition task outperform those achieved by the best system even though a simple linear classifier is used. Furthermore, integrating the learned descriptors as an additional source leads to improved performance.

Despite the success of the automatic speech recognition framework in its own application field, its adaptation to the problem of acoustic event detection has resulted in limited success. In this paper, instead of treating the problem similar to the segmentation and classification tasks in speech recognition, we pose it as a regression task and propose an approach based on random forest regression. Furthermore, event localization in time can be efficiently handled as a joint problem. We first decompose the training audio signals into multiple interleaved superframes which are annotated with the corresponding event class labels and their displacements to the temporal onsets and offsets of the events. For a specific event category, a random-forest regression model is learned using the displacement information. Given an unseen superframe, the learned regressor will output the continuous estimates of the onset and offset locations of the events. To deal with multiple event categories, prior to the category-specific regression phase, a superframe-wise recognition phase is performed to reject the background superframes and to classify the event superframes into different event categories. While jointly posing event detection and localization as a regression problem is novel, the superior performance on two databases ITC-Irst and UPC-TALP demonstrates the efficiency and potential of the proposed approach.

This paper proposes an approach for the efficient automatic joint detection and localization of single-channel acoustic events us-ing random forest regression. The audio signals are decom-posed into multiple densely overlapping superframes annotated with event class labels and their displacements to the temporal starting and ending points of the events. Using the displacement information, a multivariate random forest regression model is learned for each event category to map each superframe to con-tinuous estimates of onset and offset locations of the events. In addition, two classifiers are trained using random forest clas-sification to classify superframes of background and different event categories. On testing, based on the detection of category-specific superframes using the classifiers, the learned regressor provides the estimates of onset and offset locations in time of the corresponding event. While posing event detection and lo-calization as a regression problem is novel, the quantitative eval-uation on ITC-Irst database of highly variable acoustic events shows the efficiency and potential of the proposed approach. Index Terms: acoustic event detection, regression forest, ran-dom forest, superframe