We present a study on music emotion recognition from lyrics. We start from a dataset of 764 samples (audio+lyrics) and perform feature extraction using several natural language processing techniques. Our goal is to build classifiers for the different datasets, comparing different algorithms and using feature selection. The best results (44.2% F-measure) were attained with SVMs. We also perform ...

We present an algorithm for song composition using prosody of Japanese lyrics. Since Japanese is a “pitch accent” language, listener’s apprehension is strongly affected by the pitch motions of the speaker. For example, the meaning of Japanese word “ha-shi” changes with the pitch. It means “bridge” with an upward pitch motion, and “chopsticks” with the motion inversed. A melody attached to the l...

Understanding the lyrics of many contemporary songs is difficult. Watching the talker’s face improves speech understanding when the speech is degraded by noise or hearing difficulty. To explore whether the face can be similarly helpful in music, 34 phrases from the song “The Pressman” by Primus (1993) were played to thirteen college students. These phrases were aligned with Baldi , a computer-a...

We attack an inexplicably under-explored language genre of spoken language—lyrics in music—via completely unsupervised induction of an SMT-style stochastic transduction grammar for hip hop lyrics, yielding a fully-automatically learned challenge-response system that produces rhyming lyrics given an input. Unlike previous efforts, we choose the domain of hip hop lyrics, which is particularly uns...

The musical group the Beatles had a profound impact on Western culture during their time together between 1960 and 1970. The three songwriters, John Lennon, Paul McCartney, and George Harrison, together and separately evolved in their lyrical styles over time. Using a new generation of computer text analyses, the Beatles’ lyrics were analyzed to address how the group changed as a unit over time...

Novelty is an important psychological construct that affects both perceptual and behavioral processes. Here, we propose a lexical novelty score (LNS) for a song’s lyric, based on the statistical properties of a corpus of 275,905 lyrics (available at www.smcnus.org/lyrics/). A lyric-level LNS was derived as a function of the inverse document frequencies of its unique words. An artist-level LNS w...

In a previous study (Simmons-Stern, Budson & Ally, 2010), we found that patients with Alzheimer's disease (AD) better recognized visually presented lyrics when the lyrics were also sung rather than spoken at encoding. The present study sought to further investigate the effects of music on memory in patients with AD by making the content of the song lyrics relevant for the daily life of an older...

How individuals perceive music is influenced by many different factors. The audible part of a piece of music, its sound, does for sure contribute, but is only one aspect to be taken into account. Cultural information influences how we experience music, as does the songs’ text and its sound. Next to symbolic and audio based music information retrieval, which focus on the sound of music, song lyr...

Music emotion recognition (MER) is a key issue in user contextaware recommendation.Many existingmethods require hand-crafted features on audio and lyrics. Here we propose a new end-to-end method for recognizing emotions of tracks from their acoustic signals and lyrics via multimodal deep neural networks. We evaluate our method on about 7,000 K-pop tracks labeled as positive or negative emotion....

By adopting recent advances in music creation technologies, such as digital audio workstations and singing voice synthesizers, people can now create songs in their personal computers. Computers can also assist in creating lyrics or generating them automatically, although this aspect has been less thoroughly researched and is limited to rhyme and meter. This study focuses on the structural relat...