_Signals_: A Multidisciplinary Journal of Signal Processing Research  
[PERSON] 1,2  
1 Department of Electronics and Biomedical Engineering, University of Barcelona, Marti [PERSON] 1, 08028 Barcelona, Spain; [EMAIL_ADDRESS]  
2 Signal and Information Processing in Sensor Systems, Institute for Bioengineering of Catalonia, The Barcelona Institute of Science and Technology, Baldiri Revac 10-12, 08028 Barcelona, Spain  
Being the new editor-in-chief of _Signals_ is a great honour and a daunting task. I aspire to elevate _Signals_ as a reputable venue for original and innovative submissions in signal processing and its emerging domains. I would like to say a few words about my background. I studied physics at the University of Barcelona in the 1980s. My first contact with signal processing was in the context of what could be today described as a master's thesis. At that time, and together with my advisor (Prof. [PERSON]), we developed a linear deconvolution method for multiexponential transients that was able to estimate continuous distributions of amplitudes and time constants of the decay [1]. Then, my research steered towards silicon pressure sensors during my PhD. In my postdoc (early 1990s) at the University of Rome Tor Vergata, I had the opportunity to become acquainted with signal processing and pattern recognition techniques (including multilayer perceptrons and Self-Organizing Maps) applied to chemical sensor arrays under the supervision of Prof. [PERSON]. When I returned to the University of Barcelona, I was commissioned to prepare a basic course on signal processing for electronic engineers. When preparing for this course, I discovered the books of [PERSON] [2], and they had a large impact on me. The theory of discrete linear systems was beautiful, complete, and provided a very solid background to my previous ideas on signal processing. From there, I went deeper into the theory of random signals, and I was again impacted by the works of [PERSON] regarding the identification of non-linear systems [3]. From that point onwards, my full academic career revolved around sensors, signal processing and machine learning [4]. After my sabbatical year in AIRBUS--Munich working on Ion Mobility Spectrometry, my interest also tilted towards the application of signal processing in chemical instrumentation, an area that is certainly not mainstream in the signal processing field [5]. To me, it is very interesting to realize that similar techniques have been developed independently in different application domains. For instance, non-negative matrix factorization (NMF) [6] was previously developed for chemical instruments under the name of Multi-Curve Resolution (MCR) [7]. We can also recall the relationship between Principal Component Analysis and the Karhunen Loeve Transform [8]. Signal processing is a mature field that is transversal across many disciplines. Today, we can find applications of signal processing in audio, music and speech processing, data compression, radar and sonar, biomedical signals, seismology, vibration analysis, telecommunications, control systems, autonomous robotics, multisensor systems, fault detection, identification and correction, spectrometry and spectroscopy, and many more areas. In the last decade, machine learning and deep learning techniques have revolutionized signal processing tasks with their impressive results and versatility. However, these techniques are not flawless and can fail miserably when applied outside the training domain. Therefore, we need to pay attention to validation methods that go beyond in-dataset cross-validation and investigate transfer learning approaches. Moreover, we need to pursue interpretable machine-learning methods that avoid black-box solutions. Nevertheless, we should not neglect the solid foundations of statistical signal processing in the face of the allure and power of machine learning.