International Journal of Digital Multimedia Broadcasting

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.

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.

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.

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.

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.

With great technologies, emerge new needs and requirements. The progress achieved in Access broadband rates accentuated the demand on IPTV (Internet Protocol Television) services specially Video on Demand (VOD) and Time Shift TV (TSTV) which led subsequently to a great need for efficiency in the use of bandwidth consumed by those services. Optimization solutions are considered by IPTV service providers to lighten the service load especially on Access Nodes uplinks. This paper describes an optimization of TSTV dedicated bandwidth based on a peer-assisting TSTV content delivery, a solution in which the users STBs (set-top-boxes) assist the central TSTV servers in the service fulfillment. For this purpose, for each TSTV request, the STB will be receiving the TSTV stream from a neighbor STB instead of the central server. By using this method, the unicast traffic will not pass through the IP network; it will be a peer-to-peer communication within the access network. Extensive simulation results were included to illustrate the validity of the proposed new solution.