In this paper, we present a novel computer audition task: audio-based video game genre classification. The aim of this study is threefold: 1) to check the feasibility of the proposed task; 2) to introduce a new corpus: The Game Genre by Audio + Multimodal Extracts (G2 AME), collected entirely from social multimedia; and 3) to compare the efficacy of various acoustic feature spaces to classify the G2 AME corpus into six game genres using a linear support vector machine classifier. For the classification we extract three different feature representations from the game audio files: 1) Knowledge-based acoustic features; 2) DEEP SPECTRUM features; and 3) quantized DEEP SPECTRUM features using Bag-of-Audio-Words. The DEEP SPECTRUM features are a deep-learning-based representation derived from forwarding the visual representations of the audio instances, in particular spectrograms, mel-spectrograms, chromagrams, and their deltas through deep task-independent pretrained CNNs. Specifically, activations of fully connected layers from three common image classification CNNs, GoogLeNet, AlexNet, and VGG16 are used as feature vectors. Results for the six-genre classification problem indicate the suitability of our deep learning approach for this task. Our best method achieves an accuracy of up to 66.9% unweighted average recall using tenfold cross-validation.

In the era of deep learning, research into the classification of various components of the acoustic environment, especially in-the-wild recordings, is gaining in popularity. This is due in part to the increasing computational capacities and the expanding amount of real-world data available on social multimedia. However, the noisy nature of this data can add an additional complexity to the already complex deep learning systems. Herein, we tackle this issue by quantising deep feature representations of various in-the-wild audio data sets. The aim of this paper is twofold: 1) to assess the feasibility of the proposed feature quantisation task, and 2) to compare the efficacy of various feature spaces extracted from different fully connected deep neural networks to classify six real-world audio corpora. For the classification, we extract two feature sets: i) DEEP SPECTRUM features which are derived from forwarding the visual representations of the audio instances, in particular mel-spectrograms through very deep task-independent pre-trained Convolutional Neural Networks (CNNs), and ii) Bag-of-Deep-Features (BODF) which is the quantisation of the DEEP SPECTRUM features. Using BODF, we show the suitability of quantising the deep representations for noisy in-the-wild audio data. Finally, we analyse the effect of early and late fusion of the CNN features and models on the classification results.

The adage that there is no data like more data is not new in affective computing; however, with recent advances in deep learning technologies, such as end-to-end learning, the need for extracting big data is greater than ever. Multimedia resources available on social media represent a wealth of data more than large enough to satisfy this need. However, an often prohibitive amount of effort has been required to source and label such instances. As a solution, we introduce Cost-efficient Audio-visual Acquisition via Social-media Small-world Targeting (CAS2T) for efficient large-scale big data collection from online social media platforms. Our system is based on a unique combination of small-world modelling, unsupervised audio analysis, and semi-supervised active learning. Such an approach facilitates rapid training on entirely new tasks sourced in their entirety from social multimedia. We demonstrate the high capability of our methodology via collection of original datasets containing a range of naturalistic, in-the-wild examples of human behaviours.

In this paper, we propose a method for automatically detecting various types of snore sounds using image classification convolutional neural network (CNN) descriptors extracted from audio file spectrograms.The descriptors, denoted as deep spectrum features, are derived from forwarding spectrograms through very deep task-independent pre-trained CNNs.Specifically, activations of fully connected layers from two common image classification CNNs, AlexNet and VGG19, are used as feature vectors.Moreover, we investigate the impact of differing spectrogram colour maps and two CNN architectures on the performance of the system.Results presented indicate that deep spectrum features extracted from the activations of the second fully connected layer of AlexNet using a viridis colour map are well suited to the task.This feature space, when combined with a support vector classifier, outperforms the more conventional knowledge-based features of 6 373 acoustic functionals used in the INTERSPEECH ComParE 2017 Snoring sub-challenge baseline system.In comparison to the baseline, unweighted average recall is increased from 40.6 % to 44.8 % on the development partition, and from 58.5 % to 67.0 % on the test partition.

Humanoid robots have in recent years shown great promise for supporting the educational needs of children on the autism spectrum.To further improve the efficacy of such interactions, user-adaptation strategies based on the individual needs of a child are required.In this regard, the proposed study assesses the suitability of a range of speech-based classification approaches for automatic detection of autism severity according to the commonly used Social Responsiveness Scale™ second edition (SRS-2).Autism is characterised by socialisation limitations including child language and communication ability.When compared to neurotypical children of the same age these can be a strong indication of severity.This study introduces a novel dataset of 803 utterances recorded from 14 autistic children aged between 4 -10 years, during Wizard-of-Oz interactions with a humanoid robot.Our results demonstrate the suitability of support vector machines (SVMs) which use acoustic feature sets from multiple Interspeech COMPARE challenges.We also evaluate deep spectrum features, extracted via an image classification convolutional neural network (CNN) from the spectrogram of autistic speech instances.At best, by using SVMs on the acoustic feature sets, we achieved a UAR of 73.7 % for the proposed 3-class task.

In this paper, we propose a method for automatically detecting deceptive speech by relying on predicted scores derived from emotion dimensions such as arousal, valence, regulation, and emotion categories.The scores are derived from task-dependent models trained on the GEMEP emotional speech database.Inputs from the INTERSPEECH 2016 Computational Paralinguistics Deception sub-challenge are processed to obtain predictions of emotion attributes and associated scores that are then used as features in detecting deception.We show that using the new emotion-related features, it is possible to improve upon the challenge baseline.