Deep Learning-Based Speech Emotion Recognition Using Librosa

Speech Emotion Recognition is a challenge of computational paralinguistic and speech processing that tries to identify and classify the emotions expressed in spoken language. The objective is to infer from a speaker's speech patterns, such as prosody, pitch, and rhythm, their emotional state, such as happiness, rage, sadness, or frustration. In the modern world, one of the most crucial marketing tactics is emotion detection. For a person, you might tailor several things in order to best fit their interests. Due to this, we made the decision to work on a project where we could identify a person's emotions based just on their speech, allowing us to handle a variety of AI-related applications. Examples include the ability of call centers to play music during tense exchanges. Another example might be a smart automobile that slows down when someone is scared or furious. In Python, we processed and extracted features from the audio files using the Librosa module. A Python library for audio and music analysis is called Librosa. It offers the fundamental components required to develop systems for retrieving music-related information. Because of this, there is a lot of potential for this kind of application in the market that would help businesses and ensure customer safety

Learning deep physiological models of affect

Feature extraction and feature selection are crucial phases in the process of affective modeling. Both, however, incorporate substantial limitations that hinder the development of reliable and accurate models of affect. For the purpose of modeling affect manifested through physiology, this paper builds on recent advances in machine learning with deep learning (DL) approaches. The efficiency of DL algorithms that train artificial neural network models is tested and compared against standard feature extraction and selection approaches followed in the literature. Results on a game data corpus — containing players’ physiological signals (i.e. skin conductance and blood volume pulse) and subjective self-reports of affect — reveal that DL outperforms manual ad-hoc feature extraction as it yields significantly more accurate affective models. Moreover, it appears that DL meets and even outperforms affective models that are boosted by automatic feature selection, for several of the scenarios examined. As the DL method is generic and applicable to any affective modeling task, the key findings of the paper suggest that ad-hoc feature extraction and selection — to a lesser degree — could be bypassed.The authors would like to thank Tobias Mahlmann for his work on the development and administration of the cluster used to run the experiments. Special thanks for proofreading goes to Yana Knight. Thanks also go to the Theano development team, to all participants in our experiments, and to Ubisoft, NSERC and Canada Research Chairs for funding. This work is funded, in part, by the ILearnRW (project no: 318803) and the C2Learn (project no. 318480) FP7 ICT EU projects.peer-reviewe

Multimodal Emotion Recognition via Convolutional Neural Networks: Comparison of different strategies on two multimodal datasets

The aim of this paper is to investigate emotion recognition using a multimodal approach that exploits convolutional neural networks (CNNs) with multiple input. Multimodal approaches allow different modalities to cooperate in order to achieve generally better performances because different features are extracted from different pieces of information. In this work, the facial frames, the optical flow computed from consecutive facial frames, and the Mel Spectrograms (from the word melody) are extracted from videos and combined together in different ways to understand which modality combination works better. Several experiments are run on the models by first considering one modality at a time so that good accuracy results are found on each modality. Afterward, the models are concatenated to create a final model that allows multiple inputs. For the experiments the datasets used are BAUM-1 ((Bahçeşehir University Multimodal Affective Database - 1) and RAVDESS (Ryerson Audio–Visual Database of Emotional Speech and Song), which both collect two distinguished sets of videos based on the different intensity of the expression, that is acted/strong or spontaneous/normal, providing the representations of the following emotional states that will be taken into consideration: angry, disgust, fearful, happy and sad. The performances of the proposed models are shown through accuracy results and some confusion matrices, demonstrating better accuracy than the compared proposals in the literature. The best accuracy achieved on BAUM-1 dataset is about 95%, while on RAVDESS it is about 95.5%

Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition

It has been a hot research topic to enable machines to understand human emotions in multimodal contexts under dialogue scenarios, which is tasked with multimodal emotion analysis in conversation (MM-ERC). MM-ERC has received consistent attention in recent years, where a diverse range of methods has been proposed for securing better task performance. Most existing works treat MM-ERC as a standard multimodal classification problem and perform multimodal feature disentanglement and fusion for maximizing feature utility. Yet after revisiting the characteristic of MM-ERC, we argue that both the feature multimodality and conversational contextualization should be properly modeled simultaneously during the feature disentanglement and fusion steps. In this work, we target further pushing the task performance by taking full consideration of the above insights. On the one hand, during feature disentanglement, based on the contrastive learning technique, we devise a Dual-level Disentanglement Mechanism (DDM) to decouple the features into both the modality space and utterance space. On the other hand, during the feature fusion stage, we propose a Contribution-aware Fusion Mechanism (CFM) and a Context Refusion Mechanism (CRM) for multimodal and context integration, respectively. They together schedule the proper integrations of multimodal and context features. Specifically, CFM explicitly manages the multimodal feature contributions dynamically, while CRM flexibly coordinates the introduction of dialogue contexts. On two public MM-ERC datasets, our system achieves new state-of-the-art performance consistently. Further analyses demonstrate that all our proposed mechanisms greatly facilitate the MM-ERC task by making full use of the multimodal and context features adaptively. Note that our proposed methods have the great potential to facilitate a broader range of other conversational multimodal tasks.Comment: Accepted by ACM MM 202

The SEMAINE API: Towards a Standards-Based Framework for Building Emotion-Oriented Systems

This paper presents the SEMAINE API, an open source framework for building emotion-oriented systems. By encouraging and simplifying the use of standard representation formats, the framework aims to contribute to interoperability and reuse of system components in the research community. By providing a Java and C++ wrapper around a message-oriented middleware, the API makes it easy to integrate components running on different operating systems and written in different programming languages. The SEMAINE system 1.0 is presented as an example of a full-scale system built on top of the SEMAINE API. Three small example systems are described in detail to illustrate how integration between existing and new components is realised with minimal effort

People detection, tracking and biometric data extraction using a single camera for retail usage

Tato práce se zabývá návrhem frameworku, který slouží k analýze video sekvencí z RGB kamery. Framework využívá technik sledování osob a následné extrakce biometrických dat. Biometrická data jsou sbírána za účelem využití v malobochodním prostředí. Navržený framework lze rozdělit do třech menších komponent, tj. detektor osob, sledovač osob a extraktor biometrických dat. Navržený detektor osob využívá různé architektury sítí hlubokého učení k určení polohy osob. Řešení pro sledování osob se řídí známým postupem \uv{online tracking-by-detection} a je navrženo tak, aby bylo robustní vůči zalidněným scénám. Toho je dosaženo začleněním dvou metrik týkající se vzhledu a stavu objektu v asociační fázi. Kromě výpočtu těchto deskriptorů, jsme schopni získat další informace o jednotlivcích jako je věk, pohlaví, emoce, výška a trajektorie. Návržené řešení je ověřeno na datasetu, který je vytvořen speciálně pro tuto úlohu.This thesis proposes a framework that analyzes video sequences from a single RGB camera by extracting useful soft-biometric data about tracked people. The aim is to focus on data that could be utilized in a retail environment. The designed framework can be broken down into the smaller components, i.e., people detector, people tracker, and soft-biometrics extractor. The people detector employs various deep learning architectures that estimate bounding boxes of individuals. The tracking solution follows the well-known online tracking-by-detection approach, while the proposed solution is built to be robust regarding the crowded scenes by incorporating appearance and state features in the matching phase. Apart from calculating appearance descriptors only for matching, we extract additional information of each person in the form of age, gender, emotion, height, and trajectory when possible. The whole framework is validated against the dataset which was created for this propose