We present recent developments in the openSMILE feature extraction toolkit. Version 2.0 now unites feature extraction paradigms from speech, music, and general sound events with basic video features for multi-modal processing. Descriptors from audio and video can be processed jointly in a single framework allowing for time synchronization of parameters, on-line incremental processing as well as off-line and batch processing, and the extraction of statistical functionals (feature summaries), such as moments, peaks, regression parameters, etc. Postprocessing of the features includes statistical classifiers such as support vector machine models or file export for popular toolkits such as Weka or HTK. Available low-level descriptors include popular speech, music and video features including Mel-frequency and similar cepstral and spectral coefficients, Chroma, CENS, auditory model based loudness, voice quality, local binary pattern, color, and optical flow histograms. Besides, voice activity detection, pitch tracking and face detection are supported. openSMILE is implemented in C++, using standard open source libraries for on-line audio and video input. It is fast, runs on Unix and Windows platforms, and has a modular, component based architecture which makes extensions via plug-ins easy. openSMILE 2.0 is distributed under a research license and can be downloaded from http://opensmile.sourceforge.net/.

Abstract. We evaluate some recent developments in recurrent neural network (RNN) based speech enhancement in the light of noise-robust automatic speech recognition (ASR). The proposed framework is based on Long Short-Term Memory (LSTM) RNNs which are discriminatively trained according to an optimal speech reconstruction objective. We demonstrate that LSTM speech enhancement, even when used 'naïvely' as front-end processing, delivers competitive results on the CHiME-2 speech recognition task. Furthermore, simple, feature-level fusion based extensions to the framework are proposed to improve the integration with the ASR back-end. These yield a best result of 13.76 % average word error rate, which is, to our knowledge, the best score to date.

This paper describes an in-depth investigation of training criteria, network architectures and feature representations for regression-based single-channel speech separation with deep neural networks (DNNs). We use a generic discriminative training criterion corresponding to optimal source reconstruction from time-frequency masks, and introduce its application to speech separation in a reduced feature space (Mel domain). A comparative evaluation of time-frequency mask estimation by DNNs, recurrent DNNs and non-negative matrix factorization on the 2nd CHiME Speech Separation and Recognition Challenge shows consistent improvements by discriminative training, whereas long short-term memory recurrent DNNs obtain the overall best results. Furthermore, our results confirm the importance of fine-tuning the feature representation for DNN training.

Without doubt, there is emotional information in almost any kind of sound received by humans every day: be it the affective state of a person transmitted by means of speech; the emotion intended by a composer while writing a musical piece, or conveyed by a musician while performing it; or the affective state connected to an acoustic event occurring in the environment, in the soundtrack of a movie, or in a radio play. In the field of affective computing, there is currently some loosely connected research concerning either of these phenomena, but a holistic computational model of affect in sound is still lacking. In turn, for tomorrow’s pervasive technical systems, including affective companions and robots, it is expected to be highly beneficial to understand the affective dimensions of “the sound that something makes,” in order to evaluate the system’s auditory environment and its own audio output. This article aims at a first step toward a holistic computational model: starting from standard acoustic feature extraction schemes in the domains of speech, music, and sound analysis, we interpret the worth of individual features across these three domains, considering four audio databases with observer annotations in the arousal and valence dimensions. In the results, we find that by selection of appropriate descriptors, cross-domain arousal, and valence regression is feasible achieving significant correlations with the observer annotations of up to 0.78 for arousal (training on sound and testing on enacted speech) and 0.60 for valence (training on enacted speech and testing on music). The high degree of cross-domain consistency in encoding the two main dimensions of affect may be attributable to the co-evolution of speech and music from multimodal affect bursts, including the integration of nature sounds for expressive effects.

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