Managing uncertainty in sound based control for an autonomous helicopter

In this paper we present our ongoing research using a multi-purpose, small and low cost autonomous helicopter platform (Flyper ). We are building on previously achieved stable control using evolutionary tuning. We propose a sound based supervised method to localise the indoor helicopter and extract meaningful information to enable the helicopter to further stabilise its flight and correct its flightpath. Due to the high amount of uncertainty in the data, we propose the use of fuzzy logic in the signal processing of the sound signature. We discuss the benefits and difficulties using type-1 and type-2 fuzzy logic in this real-time systems and give an overview of our proposed system

Supervised Control of a Flying Performing Robot using its Intrinsic Sound

We present the current results of our ongoing research in achieving efficient control of a flying robot for a wide variety of possible applications. A lightweight small indoor helicopter has been equipped with an embedded system and relatively simple sensors to achieve autonomous stable flight. The controllers have been tuned using genetic algorithms to further enhance flight stability. A number of additional sensors would need to be attached to the helicopter to enable it to sense more of its environment such as its current location or the location of obstacles like the walls of the room it is flying in. The lightweight nature of the helicopter very much restricts the amount of sensors that can be attached to it. We propose utilising the intrinsic sound signatures of the helicopter to locate it and to extract features about its current state, using another supervising robot. The analysis of this information is then sent back to the helicopter using an uplink to enable the helicopter to further stabilise its flight and correct its position and flight path without the need for additional sensors

Multimodal music information processing and retrieval: survey and future challenges

Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, mid-level representations, motion, and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on the application they address. Subsequently, we analyze existing information fusion approaches, and we conclude with the set of challenges that Music Information Retrieval and Sound and Music Computing research communities should focus in the next years

Towards Anthropomorphic Robot Thereminist

Theremin is an electronic musical instrument considered to be the most difficult to play which requires the players hands to have high precision and stability as any position change within proximity of the instruments antennae can make a difference to the pitch or volume. In a different direction to previous developments of Theremin playing robots, we propose a Humanoid Thereminist System that goes beyond using only one degree of freedom which will open up the possibility for robot to acquire more complex skills, such as aerial fingering and include musical expressions in playing the Theremin. The proposed system consists of two phases, namely calibration phase and playing phase which can be executed independently. During the playing phase, the System takes input from a MIDI file and performs path planning using a combination of minimum energy strategy in joint space and feedback error correction for next playing note. Three experiments have been conducted to evaluate the developed system quantitatively and qualitatively by playing a selection of music files. The experiments have demonstrated that the proposed system can effectively utilise multiple degrees of freedoms while maintaining minimum pitch error margins

A system for recognizing human emotions based on speech analysis and facial feature extraction: applications to Human-Robot Interaction

With the advance in Artificial Intelligence, humanoid robots start to interact with ordinary people based on the growing understanding of psychological processes. Accumulating evidences in Human Robot Interaction (HRI) suggest that researches are focusing on making an emotional communication between human and robot for creating a social perception, cognition, desired interaction and sensation. Furthermore, robots need to receive human emotion and optimize their behavior to help and interact with a human being in various environments. The most natural way to recognize basic emotions is extracting sets of features from human speech, facial expression and body gesture. A system for recognition of emotions based on speech analysis and facial features extraction can have interesting applications in Human-Robot Interaction. Thus, the Human-Robot Interaction ontology explains how the knowledge of these fundamental sciences is applied in physics (sound analyses), mathematics (face detection and perception), philosophy theory (behavior) and robotic science context. In this project, we carry out a study to recognize basic emotions (sadness, surprise, happiness, anger, fear and disgust). Also, we propose a methodology and a software program for classification of emotions based on speech analysis and facial features extraction. The speech analysis phase attempted to investigate the appropriateness of using acoustic (pitch value, pitch peak, pitch range, intensity and formant), phonetic (speech rate) properties of emotive speech with the freeware program PRAAT, and consists of generating and analyzing a graph of speech signals. The proposed architecture investigated the appropriateness of analyzing emotive speech with the minimal use of signal processing algorithms. 30 participants to the experiment had to repeat five sentences in English (with durations typically between 0.40 s and 2.5 s) in order to extract data relative to pitch (value, range and peak) and rising-falling intonation. Pitch alignments (peak, value and range) have been evaluated and the results have been compared with intensity and speech rate. The facial feature extraction phase uses the mathematical formulation (B\ue9zier curves) and the geometric analysis of the facial image, based on measurements of a set of Action Units (AUs) for classifying the emotion. The proposed technique consists of three steps: (i) detecting the facial region within the image, (ii) extracting and classifying the facial features, (iii) recognizing the emotion. Then, the new data have been merged with reference data in order to recognize the basic emotion. Finally, we combined the two proposed algorithms (speech analysis and facial expression), in order to design a hybrid technique for emotion recognition. Such technique have been implemented in a software program, which can be employed in Human-Robot Interaction. The efficiency of the methodology was evaluated by experimental tests on 30 individuals (15 female and 15 male, 20 to 48 years old) form different ethnic groups, namely: (i) Ten adult European, (ii) Ten Asian (Middle East) adult and (iii) Ten adult American. Eventually, the proposed technique made possible to recognize the basic emotion in most of the cases