Song-level feature summarization is fundamental for the browsing, retrieval, and indexing of digital music archives. This study proposes a deep neural network model, CQTXNet, for extracting song-level feature summary for cover song identification. CQTXNet incorporates depth-wise separable convolution, residual network connections, and attention models to extend previous approaches. An experimental evaluation of the proposed CQTXNet was performed on two publicly available cover song datasets by varying the number of network layers and the type of attention modules.

In this paper, we propose a power amplifier linearization technique combined with iterative noise cancelation. This method alleviates the effect of added noises which prevents the predistorter (PD) from estimating the exact characteristics of the power amplifier (PA). To iteratively cancel the noise added in the feedback signal, the output signal of the power amplifier without noise is reconstructed by applying the inverse characteristics of the PD to the predistorted signals. The noise can be revealed by subtracting the reconstructed signals from the feedback signals. Simulation results based on the mean-square error (MSE) and power spectral density (PSD) criteria are presented to evaluate PD performance. The results show that the iterative noise cancelation significantly enhances the MSE performance, which leads to an improvement of the out-of-band power suppression. The performance of the proposed technique is verified by computer simulation and hardware test results.

The second generation digital terrestrial broadcasting system (DVB-T2) is the first broadcasting system employing MISO (Multiple-Input Single-Output) algorithms. The potential MISO gain of this system has been roughly predicted through simulations and field tests. Of course, the potential MISO SFN gain (MISO-SFNG) differs according to the simulation conditions, test methods, and measurement environments. In this paper, network gains of SISO-SFN and MISO-SFN are theoretically derived. Such network gains are also analyzed with respect to the receive power imbalance and coverage distances of SISO and MISO SFN. From the analysis, it is proven that MISO-SFNG is always larger than SISO SFN gain (SISO-SFNG) in terms of the achievable SNR. Further, both MISO-SFNG and SISO-SFNG depend on the power imbalance, but the network gains are constant regardless of the modulation order. Once the field strength of the complete SFN is obtained by coverage planning tools or field measurements, the SFN service coverage can be precisely calibrated by applying the closed-form SFNG formula.

A simple detector named QR-LRL for MIMO systems was proposed in and it was shown that QR-LRL approached the hard-output ML performance. However, its soft-output performance is not capable of approaching the near ML performance. In this letter, we propose a novel detection method which can generate reliable soft-outputs while avoiding the empty vector set problem. The proposed detector efficiently uses the upper triangular structure in QR decomposition. Simulation results show that the proposed detector can approach the near soft-output ML performance as well as hard-output with feasible complexity.