## Contributions  
### Speech Analysis and Enhancement  
The selection includes two papers related to speech analysis and enhancement, a traditional key topic in the area of speech processing, which are focused on the automatic discrimination of singing from speech, and the improvement of speech recognition systems running on two-microphone smartphones, respectively.  
The article by [PERSON] et al. [2] introduces an automatic classification system capable of discriminating speech from singing voice, using two parameters derived from their fundamental frequency. This research derives from the need to separate speech segments from a capella singing segments recorded in Bertsolarism (Bertsolaritza in Basque), the traditional art of live improvising sung poems in the Basque Country. The proposal is developed on the Bertsolaritza database, and it is subsequently tested on this dataset and on the English Sung and Spoken Lyrics Corpus from the National University of Singapore. The proposal combines a Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) based voice activity detection, a pitch-based Note Detection Algorithm (as a by-product of the discrimination approach) and a binary GMM-based classifier that differentiates speech from singing. The system obtains good results when discriminating both voice types, obtaining a better performance than the baseline systems based on pitch parameters, and similar results as the spectrum analysis system. Finally, the proposal shows good generalization capabilities, besides entailing a low computation time.  
The article by [PERSON] et al. [3] deals with speech enhancement in dual-microphone smartphones using beamforming along with post-filtering techniques. In this context, it is worth mentioning that the performance of beamforming algorithms is diminished on dual-microphone smartphones mainly due to the short number of microphones, the close separation between them and their placement on the device. In order to improve the estimation of the acoustic channel and the main characteristics of speech and noise when using the Minimum Variance Distortionless Response (MVDR) beamformer, the authors propose combining the estimation of the relative transfer function (RTF) between microphones using an extended Kalman filter (eKF) together with a speech presence probability (SPP) estimator intended to track the background noise variability. Moreover, the proposal also includes postfiltering that make use of the previously estimated parameters to design a gain function which is applied to the beamformer output signal for further noise reduction. The experiments evaluate the proposal in different reverberant and noisy environments, considering both close-talk and far-talk environments and simulating dual-channel noisy speech recordings from a dual-microphone smartphone positions using the English TIMIT database as input. The results show that the proposal improves the estimation of RTFs and SPPs with respect to state-of-the-art algorithms, since it yields lower speech distortion and better speech quality and intelligibility. Moreover, the proposed postfilters also are capable of improving the noise reduction of the MVDR-based output compared to those algorithms specifically intended for dual-microphone smartphones without degrading the speech intelligibility.