La traducció del Càntic dels Càntics de Carles Riba

Carles Riba’s activity as a translator of Greek classics (Xenophon, Plutarch, Homer, Sophocles, Euripides, Aeschylus) and works of important authors who wrote in Latin (Virgil), English (Edgar A. Poe) and German (Rilke, Hölderlin) is well known and has been widely studied. In contrast, the great humanist’s translations of books of the Bible -Song of Songs and the Book of Ruth- from Hebrew to Catalan have never been the subject of a monographic study. This piece of work is an edition and a detailed analysis of his version of the Song of Solomon. The notes in the text point out the translator’s contributions and uncertainties in a work published at a pivotal time in the Catalan language’s history

De la nostalgia a la reflexión : un recorrido por "La canción de la pena eterna", de Wang Anyi

La novela "La canción de la pena eterna" (1995), de la escritora Wang Anyi, es considerada actualmente un clásico moderno de las letras chinas. El objetivo de este trabajo se articula sobre la base de dos orientaciones. Por un lado, se realiza un extenso recorrido interpretativo por la novela, atendiendo a distintos criterios para extraer conclusiones sobre la motivación e intencionalidad de la escritora. Por otro lado, a partir de esas conclusiones, se sitúa la novela en el canon literario chino, en virtud de su propia caracterización, las influencias recibidas y su posible contribución.

Predictability of music descriptor time series and its application to cover song detection

Intuitively, music has both predictable and unpredictable components. In this work we assess this qualitative statement in a quantitative way using common time series models fitted to state-of-the-art music descriptors. These descriptors cover different musical facets and are extracted from a large collection of real audio recordings comprising a variety of musical genres. Our findings show that music descriptor time series exhibit a certain predictability not only for short time intervals, but also for mid-term and relatively long intervals. This fact is observed independently of the descriptor, musical facet and time series model we consider. Moreover, we show that our findings are not only of theoretical relevance but can also have practical impact. To this end we demonstrate that music predictability at relatively long time intervals can be exploited in a real-world application, namely the automatic identification of cover songs (i.e. different renditions or versions of the same musical piece). Importantly, this prediction strategy yields a parameter-free approach for cover song identification that is substantially faster, allows for reduced computational storage and still maintains highly competitive accuracies when compared to state-of-the-art systems.

Translating and analysing songs: three songs in the context of communist Romania

Songs were the means used by the Romanian Communist Party to ‘educate’ Romanians. Through them, Romanians were told what they had to appreciate, how grateful they were supposed to be to the regime, how great President Ceausescu was and how they had to work harder and harder so that they could be even better Communists. This paper comprises the translation of three songs composed, performed and broadcast in Communist Romania and their analysis from the point of view of communication. In translating the song, I have chosen to translate closest to the original possible meaning and meanwhile to respect to the best of my ability Low’s ‘pentathlon principle’: singability, rhyme, rhythm, naturalness and fidelity to the sense of the source text

Cross recurrence quantification for cover song identification

There is growing evidence that nonlinear time series analysis techniques can be used to successfully characterize, classify, or process signals derived from realworld dynamics even though these are not necessarily deterministic and stationary. In the present study we proceed in this direction by addressing an important problem our modern society is facing, the automatic classification of digital information. In particular, we address the automatic identification of cover songs, i.e. alternative renditions of a previously recorded musical piece. For this purpose we here propose a recurrence quantification analysis measure that allows tracking potentially curved and disrupted traces in cross recurrence plots. We apply this measure to cross recurrence plots constructed from the state space representation of musical descriptor time series extracted from the raw audio signal. We show that our method identifies cover songs with a higher accuracy as compared to previously published techniques. Beyond the particular application proposed here, we discuss how our approach can be useful for the characterization of a variety of signals from different scientific disciplines. We study coupled Rössler dynamics with stochastically modulated mean frequencies as one concrete example to illustrate this point.

Chroma binary similarity and local alignment applied to cover song identification

We present a new technique for audio signal comparison based on tonal subsequence alignment and its application to detect cover versions (i.e., different performances of the same underlying musical piece). Cover song identification is a task whose popularity has increased in the Music Information Retrieval (MIR) community along in the past, as it provides a direct and objective way to evaluate music similarity algorithms.This article first presents a series of experiments carried outwith two state-of-the-art methods for cover song identification.We have studied several components of these (such as chroma resolution and similarity, transposition, beat tracking or Dynamic Time Warping constraints), in order to discover which characteristics would be desirable for a competitive cover song identifier. After analyzing many cross-validated results, the importance of these characteristics is discussed, and the best-performing ones are finally applied to the newly proposed method. Multipleevaluations of this one confirm a large increase in identificationaccuracy when comparing it with alternative state-of-the-artapproaches.