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The main purpose in mineral processing plant is to obtain the highest possible value from a processed raw material.In majority of concentrators in the world mining the first step of the processing technology is sieving of orethat is performed using vibrating screens. Usually, in the whole technological system there are only several such machines. That is why their continuous operation is critical and they are often concerned as a bottleneck of the whole system. Although the vibrating screens are expected to keep the highest reliability indicators, their maintenance is still very often carried out using a planned and preventive strategy. Usually the diagnostics of such systems is based on vibro-acoustic methods. But the collected vibrational signals may be significantly disrupted be large pieces of ore hitting the screen. That’s why the development of diagnostic techniques, especially for rotational elements, should take into account validation methods associated with the identification of processed material impact on the screen and its elimination from the structure of the vibrational signal. Paper presents a method for detecting large rocks with the help of computer vision and audio signal processing. Usage of two sources of data makes possible cross validation of the results obtained by particular methods, which increases the robustness of the algorithm. The main purpose of the algorithm is to filter the vibrational signals from the screen for further analysis. [ABSTRACT FROM AUTHOR]

This paper describes classification of sound recordings based on their audio features. This is useful for querying large datasets, searching for recordings with some desired content. We use musical recordings as well as birdsongs recordings, which usually have rich structure and contain a lot of patterns suitable for classification. We present two different classification methods, one for musical recordings and one for birdsongs. These methods are compared and their differences are discussed. We use feature vectors that capture the audio content of recording as a whole piece and then classify these feature vectors using combination of the Self-organizing map and the Learning Vector Quantization, which represent a powerful algorithm using unlabeled as well as labeled data. In case of birdsongs we use feature vectors representing time frames of a recording. [ABSTRACT FROM PUBLISHER]

Sound source localization is an important feature designed and implemented on robots and intelligent systems. Like other artificial audition tasks, it is constrained to multiple problems, notably sound reflections and noises. This paper presents a sound source azimuth estimation approach in reverberant environments. It exploits binaural signals in a humanoid robotic context. Interaural Time and Level Differences (ITD and ILD) are extracted on multiple frequency bands and combined with a neural network-based learning scheme. A cue filtering process is used to reduce the reverberations effects. The system has been evaluated with simulation and real data, in multiple aspects covering realistic robot operating conditions, and was proven satisfying and effective as will be shown and discussed in the paper. [ABSTRACT FROM PUBLISHER]

This paper proposes a sound localization algorithm inspired by a cross-channel algorithm first studied by MacDonald et. al in 2008. The original algorithm assumes that the Head Related Transfer Functions (HRTFs) of the robotic head under study are precisely known, which is rarely the case in practice. Following the idea that any head is more or less spherical, the above assumption is relaxed by using HRTFs computed using a simple spherical head model with the same head radius as the robot head. In order to evaluate the proposed approach in realistic noisy conditions, an isotropic noise field is also computed and a precise definition of the Signal to Noise Ratio (SNR) in a binaural context is outlined. All these theoretical developments are finally assessed with simulated and experimental signals. Despite its simplicity, the proposed approach appears to be robust to noise and to provide reliable sound localization estimations in the frontal azimuthal plane. [ABSTRACT FROM PUBLISHER]

This paper describes a hands-free speech/sound recognition system developed and evaluated in the framework of the CompanionAble European Project. The system is intended to work continuously on a distant wireless microphone and detect not only vocal commands but also everyday life sounds. The proposed architecture and the description of each module are outlined. In order to have good recognition and rejection rates, some constraints were defined for the user and the vocabulary was limited. First results are presented; currently project trials are underway. [ABSTRACT FROM PUBLISHER]

Sound source localization is a need for robotic systems interacting with acoustically-active environments. In this domain, numerous binaural localization studies have been conducted within the last few decades. This paper provides an overview of a number of binaural localization cue extraction techniques. These are carefully addressed and applied on a simulated binaural database. Cues are evaluated in azimuth estimation and their discriminatory effectiveness is studied as a function of the reverberation time with statistical data analysis techniques. Results show that big differences exist between the discriminatory abilities of multiple types of cue extraction methods. Thus a careful cue selection must be performed before establishing a sound localization system. [ABSTRACT FROM PUBLISHER]

A fundamental task for a robotic audition system is sound source localization. This paper addresses the localization problem in a robotic humanoid context, providing a novel learning algorithm that uses binaural cues to determine the sound source's position. Sound signals are extracted from a humanoid robot's ears. Binaural cues are then computed to provide inputs for a neural network. The neural network uses pixel coordinates of a sound source in a camera image as outputs. This learning approach provides good localization performances as it reaches very small errors for azimuth and elevation angles estimates. [ABSTRACT FROM PUBLISHER]

This paper presents a surveillance system which alerts when controversy or an intruder is detected by using CCTV. The system takes advantage of sound signal processing to identify fighting and image processing to detect an intruder, then will notify and sent a signal to the system administer when an event occurs. The result shows that the energy of sound signal during fighting is higher than when in normal situation and the change pixel is increased when there is an intruder. According to the result, this concept is applied to CCTV system in order to avoid human fatigue leading to the error to work quality after working with the monitor for long hours. [ABSTRACT FROM PUBLISHER]