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International Journal of Digital Multimedia Broadcasting provides a forum for engineers and researchers whose interests are in digital multimedia broadcasting to share recent developments and challenges in order to design new and improved systems.
International Journal of Digital Multimedia Broadcasting maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.
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Capacity Improvement for DVB-NGH with Dual-Polarized MIMO Spatial Multiplexing and Hybrid Beamforming
Multiple-input multiple-output (MIMO) antenna scheme is an effective technique for future terrestrial broadcasting systems such as digital video broadcasting-next-generation handheld (DVB-NGH) to overcome the limits on information theory of traditional single-antenna wireless communications without any additional bandwidth or increased transmit power. In this paper, we propose a hybrid beamforming scheme for dual-polarized MIMO spatial multiplexing DVB-NGH broadcasting systems. The system of interest makes use of phase shifters and amplitude attenuators for the digital-analog precoder at beamforming stage of the transmitter end to maximize the signal-to-noise ratio to increase the channel capacity of the DVB-NGH systems. At the receiver end, the maximum likelihood (ML) detector is used to evaluate the system performance. We consider the signal-to-interference-and-noise ratio (SINR) and the achievable average capacity for the DVB-NGH MIMO with various FFT sizes, number of transmit antennas, and different modulation schemes. The performance results on bit error rate, channel capacity, and beampatterns show that the proposed hybrid beamforming and dual-polarized MIMO spatial multiplexing schemes provide more robustness against signal interference by beamforming and/or nulling techniques. The simulation results also show that the proposed system achieves higher capacity than the existing MIMO schemes for the DVB-NGH systems.
A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum
In a cognitive radio network (CRN), spectrum sensing is an important prerequisite for improving the utilization of spectrum resources. In this paper, we propose a novel spectrum sensing method based on deep learning and cycle spectrum, which applies the advantage of the convolutional neural network (CNN) in an image to the spectrum sensing of an orthogonal frequency division multiplex (OFDM) signal. Firstly, we analyze the cyclic autocorrelation of an OFDM signal and the cyclic spectrum obtained by the time domain smoothing fast Fourier transformation (FFT) accumulation algorithm (FAM), and the cyclic spectrum is normalized to gray scale processing to form a cyclic autocorrelation gray scale image. Then, we learn the deep features of layer-by-layer extraction by the improved CNN classic LeNet-5 model. Finally, we input the test set to verify the trained CNN model. Simulation experiments show that this method can complete the spectrum sensing task by taking advantage of the cycle spectrum, which has better spectrum sensing performance for OFDM signals under a low signal-noise ratio (SNR) than traditional methods.
Identification of Weakly Pitch-Shifted Voice Based on Convolutional Neural Network
Pitch shifting is a common voice editing technique in which the original pitch of a digital voice is raised or lowered. It is likely to be abused by the malicious attacker to conceal his/her true identity. Existing forensic detection methods are no longer effective for weakly pitch-shifted voice. In this paper, we proposed a convolutional neural network (CNN) to detect not only strongly pitch-shifted voice but also weakly pitch-shifted voice of which the shifting factor is less than ±4 semitones. Specifically, linear frequency cepstral coefficients (LFCC) computed from power spectrums are considered and their dynamic coefficients are extracted as the discriminative features. And the CNN model is carefully designed with particular attention to the input feature map, the activation function and the network topology. We evaluated the algorithm on voices from two datasets with three pitch shifting software. Extensive results show that the algorithm achieves high detection rates for both binary and multiple classifications.
Subjective Evaluation of Music Compressed with the ACER Codec Compared to AAC, MP3, and Uncompressed PCM
Audio data compression has revolutionised the way in which the music industry and musicians sell and distribute their products. Our previous research presented a novel codec named ACER (Audio Compression Exploiting Repetition), which achieves data reduction by exploiting irrelevancy and redundancy in musical structure whilst generally maintaining acceptable levels of noise and distortion in objective evaluations. However, previous work did not evaluate ACER using subjective listening tests, leaving a gap to demonstrate its applicability under human audio perception tests. In this paper, we present a double-blind listening test that was conducted with a range of listeners (N=100). The aim was to determine the efficacy of the ACER codec, in terms of perceptible noise and spatial distortion artefacts, against de facto standards for audio data compression and an uncompressed reference. Results show that participants reported no perceived differences between the uncompressed, MP3, AAC, ACER high quality, and ACER medium quality compressed audio in terms of noise and distortions but that the ACER low quality format was perceived as being of lower quality. However, in terms of participants’ perceptions of the stereo field, all formats under test performed as well as each other, with no statistically significant differences. A qualitative, thematic analysis of listeners’ feedback revealed that the noise artefacts that produced the ACER technique are different from those of comparator codecs, reflecting its novel approach. Results show that the quality of contemporary audio compression systems has reached a stage where their performance is perceived to be as good as uncompressed audio. The ACER format is able to compete as an alternative, with results showing a preference for the ACER medium quality versions over WAV, MP3, and AAC. The ACER process itself is viable on its own or in conjunction with techniques such as MP3 and AAC.
Projection Analysis Optimization for Human Transition Motion Estimation
It is a difficult task to estimate the human transition motion without the specialized software. The 3-dimensional (3D) human motion animation is widely used in video game, movie, and so on. When making the animation, human transition motion is necessary. If there is a method that can generate the transition motion, the making time will cost less and the working efficiency will be improved. Thus a new method called latent space optimization based on projection analysis (LSOPA) is proposed to estimate the human transition motion. LSOPA is carried out under the assistance of Gaussian process dynamical models (GPDM); it builds the object function to optimize the data in the low dimensional (LD) space, and the optimized data in LD space will be obtained to generate the human transition motion. The LSOPA can make the GPDM learn the high dimensional (HD) data to estimate the needed transition motion. The excellent performance of LSOPA will be tested by the experiments.
Research on Wireless Positioning Technology Based on Digital FM Broadcasting
With more and more new mobile devices (such as mobile phones, tablets, and wearable devices) entering people’s daily life, along with the application and development of relevant technologies based on users’ location information, location based service is becoming a basic service demand of people’s life. This paper puts forward a research on location technology based on frequency modulation band digital audio broadcasting (FM China Digital Radio, FM-CDR). A new method of adding timestamp information to the FM-CDR frame structure is proposed, which verified that the change to the system does not affect the normal transmission and reception of broadcast content under the original standards and can accurately extract the recognition signal and timing information of BS. In the complex environment, the estimation algorithm of signal parameters such as received signal strength (RSS), time of arrival (TOA), and time difference of arrival (TDOA) of terrestrial radio broadcast signals is studied. In this paper, a new method based on multisource data fusion is proposed, which can meet the need of localization in various environments and overcome the deficiency of single localization method.