Beekeeping is one of the widespread and traditional fields in agriculture, where Internet of Things (IoT)-based solutions and machine learning approaches can ease and improve beehive management significantly. A particularly important activity is bee swarming. A beehive monitoring system can be applied for digital farming to alert the user via a service about the beginning of swarming, which requires a response. An IoT-based bee activity acoustic classification system is proposed in this paper. The audio data needed for acoustic training was collected from the Open Source Beehives Project. The input audio signal was converted into feature vectors, using the Mel-Frequency Cepstral Coefficients (with cepstral mean normalization) and Linear Predictive Coding. The influence of the acoustic background noise and denoising procedure was evaluated in an additional step. Different Hidden Markov Models’ and Gaussian Mixture Models’ topologies were developed for acoustic modeling, with the objective being to determine the most suitable one for the proposed IoT-based solution. The evaluation was carried out with a separate test set, in order to successfully classify sound between the normal and swarming conditions in a beehive. The evaluation results showed that good acoustic classification performance can be achieved with the proposed system.

Animal activity acoustic monitoring is becoming one of the necessary tools in agriculture, including beekeeping. It can assist in the control of beehives in remote locations. It is possible to classify bee swarm activity from audio signals using such approaches. A deep neural networks IoT-based acoustic swarm classification is proposed in this paper. Audio recordings were obtained from the Open Source Beehive project. Mel-frequency cepstral coefficients features were extracted from the audio signal. The lossless WAV and lossy MP3 audio formats were compared for IoT-based solutions. An analysis was made of the impact of the deep neural network parameters on the classification results. The best overall classification accuracy with uncompressed audio was 94.09%, but MP3 compression degraded the DNN accuracy by over 10%. The evaluation of the proposed deep neural networks IoT-based bee activity acoustic classification showed improved results if compared to the previous hidden Markov models system.

This paper presents a proposed system for acoustic monitoring and classification of bee activity, based on audio modality. Beekeeping is one of the agriculture sectors, where ICT solutions provide great benefit for production and animal welfare. The presented system is a general classifier, which can classify normal and swarm bee activity. The captured audio signal is transformed into mel-frequency cepstral coefficients, which are used to train one state hidden Markov models. The activity classification is carried out in a form of recognition architecture, based on principles applied to speech modality. The acoustic models were trained in a two-stage approach using the open audio data provided by the Open Source Beehives Project. The highest classification accuracy of 80.89% was achieved with the proposed general classification approach.

The development of big data, machine learning, and the Internet of Things has led to rapid advances in the research field of Active and Assisted Living (AAL). A human is placed in the center of such an environment, interacting with different modalities while using the system. Although video still plays a dominant role in AAL technologies, audio, as the most natural means of interaction, is also used commonly, either as a single source of information, or in combination with other modalities. Despite the rapidly increased research efforts in the last decade, there is a lack of systematic overview of audio based technologies and applications in AAL. This review tries to fill this gap, and identifies five major topics where audio is an essential AAL building block: Physiological monitoring, emotion recognition in the context of AAL, human activity recognition, fall detection, and food intake monitoring. We address the data work flow and standard sensing technologies for capturing audio in the AAL environment, provide a comprehensive overview of audio-based AAL applications, and identify datasets available to the research community. Finally, we address the main challenges that should be handled in the upcoming years, and try to identify the potential future trends in audio-based AAL.