Favorite Songs Research Articles

Changing Your Love of Music by Stimulating the Brain

For many of us, listening to our favorite songs makes us feel happy, energetic, and inspired. One goal of brain research is to understand the role of the brain as we listen to music. For years, researchers have hypothesized that the brain areas involved in processing the music we hear are involved in musical pleasure—but they had no hard proof. By stimulating people’s brains using strong magnets, along with a brain-imaging technique that could see which areas of the brain were active, scientists have unraveled why we feel so awesome when we listen to our favorite jams. Now there is good evidence to show that our favorite tunes involve connections between specific brain areas, including the brain’s “reward center”—an area that makes us feel good when we do something that we like. These brain circuits provide a solid piece to this complex puzzle of why music makes us feel so good.

Remixing Special Education Practices with Artificial Intelligence: UDL, EBP, and HLPs

This article presents engaging and practical methods of helping educators to “remix” the evidence-based and high-leverage practices they are already familiar with to include the new capabilities of artificial intelligence (AI). Transformational modern technologies can be powerful and disruptive, possessing the potential to impact multiple areas of society, including education. One of the best ways for educators to implement AI in their teaching is using it to help support and extend their current practices (Mishra et al., 2023). Similar to how a new remix on the radio can make an old favorite song fresh again, educators can use AI to uphold and enhance their existing instructional strategies and skills. However, adapting to the new paradigm of AI in education may be challenging for teacher preparation programs in special education. The authors of this article apply some of the strategies from “Leveraging Emerging Technology to Design an Inclusive Future with Universal Design for Learning” (McMahon & Walker, 2019) to effectively implement AI in education. Rather than needing to start fresh or relearn how to teach while incorporating AI, teachers can view this article as a foundation for how to apply AI tools to support current practice. The recommendations are based on Universal Design for Learning (UDL) and strategies for adapting AI tools to support high-leverage practices and established evidence-based practices. The authors aim to inspire special educators to start using AI to help “remix” and innovate the implementation of their existing instructional strategies.

Tree of Life Model and other Africa-centred interventions

The Tree of Life is a four-part process which involves drawing trees and then there is a second part of the methodology which is about the Forest of Life. So, Tree of Life begins with inviting people to draw their tree. And each part of the tree represents something specific about their lives, their roots, where they come from, their history, their heritage, their ancestry, the ground where they live, who they live with, what they like to do when they’re at home, favourite places, favourite songs or dances. The trunk – the skills and their knowledges, the things they’re good at, the things that people tell them they’re good at. The leaves of their tree – most important people in their lives, people who care for them and support them. And then the branches – your hopes, your dreams for both the near and the distant future. And then the fruits are the gifts that you’ve received and that you’ve been given. And so, people are invited to come up with a tree and then they are supported to tell stories around these aspects of their lives by somebody who has been trained, who knows how to help them to thicken their stories, because we want people to stand firmly in the second stories of their lives and the alternative stories – the stories about your hopes, your dreams, your skills, the people that are important to you, your aspirations in your life. HOW TO REFERENCE USING ASWDNET STYLE Ncube-Mlilo N. (2024). Tree of Life Model and other Africa-centred interventions. African Journal of Social Work, 14(3), 149-154. https://dx.doi.org/10.4314/ajsw.v14i3.6 Visit journal website: https://ajsw.africasocialwork.net

Glean lessons in leadership from Elvis Presley

I’ve always loved the album Graceland by Paul Simon. It came out when I was in my 20s. I listened to those songs on vinyl. My favorite song, of course, was the title song, “Graceland.” I knew it was a place in Memphis, Tennessee, but didn’t really pay much attention to it.

In Case You Haven't Heard…

A new study suggests that eating pasta makes you happier. The Behavior & Brain Lab at the Free University of Languages and Communication (IULM) in Milan conducted a study to determine whether eating pasta could improve one's mood, New Jersey Digest reported on Jan. 26. Not only were their results positive, but researchers even concluded that eating a bowl of your favorite pasta produces similarly positive emotions to hearing your favorite song. According to Food & Wine, IULM recruited 40 participants ages 25 to 55. Researchers then measured their physical and neurological changes as they ate bowls of pasta — specifically, their favorite types. Comparatively, IULM also measured the neurological and physical changes that occurred while listening to their favorite music or watching their favorite sports teams. The researchers found that eating pasta causes a positive emotional‐cognitive state with the same results (if not higher) than those recorded while listening to music and watching sports. Similarly, pasta outperformed on the positive emotions front. Studying each participant's facial expressions, pasta produced the best results.

“My Playlist is Too (Damn) Long”: The Impact of Black Men’s Favorite Sex Songs on Sexual Socialization

Abstract: Music has historically and culturally served as a tool for Black men to express themselves and tell their stories. Further, music is an integral part of Black culture that provides unique insight into Black men’s ideal sexualities, sexual practices, and their conceptualizations of sex. Thus, this content analysis analyzed the lyrics of Black men’s favorite sex songs from a strength-based, sex-positive perspective to better conceptualize Black men’s sexualities and sexual practices. One hundred six men answered the short-form survey question, “What is your favorite sex song?” Analysis of the song content determined Black men’s favorite sex songs were focused on mutual (29%) and their partner’s pleasure (28%). Black men’s favorite sex songs focused on attentiveness and admiration of partner, fulfilling fantasy, lust and yearning, vocal affirmation and reassurance, risky sex, and desire for intimacy. These findings suggest Black men desire reciprocity, intimacy, and vulnerability during sex.

What can we learn about leadership from Elvis Presley?

I’ve always loved the album Graceland by Paul Simon. It came out when I was in my 20s. I listened to those songs on vinyl. My favorite song, of course, was the title song, “Graceland.” I knew it was a place in Memphis, Tennessee, but didn’t really pay much attention to it.

Emotion Detection with CNN Model and Song Recommendations using Machine Learning Techniques

Music is an exemplary tool to judge a person’s emotional state. It is the language of the soul. What cannot be articulated through words are easily conveyed through a melody. Music not only speaks to a person’s emotional and mental state, but it is also known to have a therapeutic effect on the listener. The traditional method of music recommendation uses collaborative or content-based filtering to recommend songs but a person’s song choices does not depend only on the song they usually listen to but depends mostly on their emotional state. With the fast-paced innovations pertaining to the music application industry, there is still scope of further improvement in the user experience and creating an encompassing application that not only allows the app users to enjoy listening to their favorite songs but also caters to their recommendation based on their emotional state. Thus, an emotion detection system using Convolution Neural Networks has been proposed. The user feeds in a custom playlist containing a mixture of musical genres that are classified into different emotions using K-Means Clustering. The CNN model detects the emotional state of the user and recommends a series of songs from the classified playlist. This interactive interface is a revolutionary innovation for users who need song recommendations that suit their current mind-state.

‘I like the song and nice video’: a pupil-led activity in the young learner EFL classroom with implications for pedagogy

ABSTRACT Design and content of classroom activities are crucial to foreign/second (L2) language learners’ motivation, be they children or adults. Lifeworld relevance and learner autonomy are two important aspects in building motivational L2 curricula for adults (e.g.), but such considerations rarely filter through to primary school learners due to institutional-curricular priorities. Presenting a single-case micro-analysis of the interactional practices that ensue from a pupil’s presentation of her favourite song and music video in a 4th grade English as a Foreign Language (EFL) classroom in Denmark, this paper showcases an example of a classroom activity with a participation framework that is sustained by the pupils’ participation and substantiated by the pupils’ own interests without losing pedagogical impetus. Our data come from an audio-video corpus of EFL classes in six Danish primary schools.

Sparrow's Nest Feeds Body & Spirit of Cancer Patients & Families

Sparrow's Nest Feeds Body & Spirit of Cancer Patients & Families

Village Songs and the Building of Community Culture: A Talk

Village Songs and the Building of Community Culture: A Talk

Mick Moloney (1944–2022)

Mick Moloney (1944–2022)

Music at Workplace: Is it trully Improving Employees’ Performance?

The purpose of this study was to compare the performance of employees who listened to music while working to those who did not. This study was conducted at the Business Simulation Laboratory, Department of Business Administration of Ujung Pandang State Polytechnic. This study employed an Experimental Research Method with a quantitative methodology. The total participants, 94, were divided into 2 groups, employees with treatment (listening to the music while working) 48 and employees without treatment, with 46 participants. The study's empirical findings indicated that there were productivity differences between employees who got the treatments (listening to music while working) compared to those who did not. The employee who works with music in the workplace performed a more excellent average performance than those who work without music. Listening to favorite songs encourages employees to improve their work efficiency.

Isabelle Compagnon.

"A turning point in my career was ten years ago, when I decided to apply the tools and concepts of my physical chemistry background to carbohydrates with the ambition to deploy new analytical tools for glycomics … My favorite song is Sugar, Sugar by The Archies, what else?!" Find out more about Isabelle Compagnon in her Introducing … Profile.

Isabelle Compagnon

Graphical Abstract „A turning point in my career was ten years ago, when I decided to apply the tools and concepts of my physical chemistry background to carbohydrates with the ambition to deploy new analytical tools for glycomics … My favorite song is Sugar, Sugar by The Archies, what else?!“ Find out more about Isabelle Compagnon in her Introducing … Profile.

The Implementation of a Module “Sahabatku Indonesia” in Favorite Song Units on the Speaking Skills of BIPA Students

Indonesian for Foreign Speakers (BIPA) is an Indonesian language teaching program whose teaching subjects are foreign speakers. The BIPA learning process certainly requires an appropriate and interesting curriculum, teaching materials, and learning methods for the success of foreign speakers in achieving learning goals. The research objective was to find out and describe the effect of the Indonesian best friend module in favorite song units on speaking skills at BIPA level 7. The research method used by researchers is descriptive qualitative. Researchers will describe a data analysis with the method used is descriptive. The data collection technique used is the form of collecting data by observing students. The results of observations on the material in the Indonesian best friend module activities are designed according to level, one of which is the advanced level for students who can already speak Indonesian, namely using the best friend Indonesia BIPA level 7 module which includes the favorite song units. In this study, the researcher refers to SKL (Graduation Competency Standards) at BIPA Level 7 through a Module that focuses on aspects of speaking skills in BIPA learners. The results of this research make students interested in the learning provided by the teacher in using the best friend module Indonesia BIPA level 7 favorite song unit and students prefer speaking skills because learning through speaking skills makes students more active, it is very fun to sing together the same as the title of the song "Laskar Pelangi" by Nidji and understand every meaning and vocabulary in the lyrics of the song.

P. Dickinson (ed.), 'Chamber Music (James Joyce). Thirty-two Songs for High Voice and Piano by G. Molyneux Palmer' (2020); 'James Joyce’s Favourite Songs: Chamber Music/The Joyce Book', M. Hill, P. Dickinson & M. Dickinson (Heritage Records, 2020)

A review of Peter Dickinson (ed.), Chamber Music (James Joyce). Thirty-two Songs for High Voice and Piano by G. Molyneux Palmer (Tewkesbury: Goodmusic Publishing, 2020). ISMN M-2223-1494-8; James Joyce’s Favourite Songs: Chamber Music / The Joyce Book; Martyn Hill, tenor & Peter Dickinson, piano (Chamber Music); Meriel Dickinson, mezzo & Peter Dickinson, piano (Joyce Book) (CD, Heritage Records HTGCD 175, 2020).

Music Consumption on Digital Platforms: A Study on Bangladeshi University Students

Because of advancements in information technology and the emergence of various digital platforms, there have been significant changes in the way people consume music over the years. Gramophones, audio cassette players, CD and DVD players, and other analog devices that were popular in the past, have become nearly obsolete. The internet and, later, smartphones were the primary drivers of change in the late 1990s. Since the mid-2000s, social media platforms like Facebook and YouTube, as well as online audio stores and streaming platforms like Spotify, iTunes, Amazon Music, Pandora, and SoundCloud, have become the most popular ways for people to listen to music on their smartphones. The internet has a much larger selection of music than traditional sources such as vinyl records, audio cassettes, CD-DVDs, or iPods, which have limited song storage capacities. This study focuses on the growing popularity of online music consumption among Bangladeshi university students. Apart from that, the study sought to explore how university students enjoy music on digital platforms, the platforms they use, whether they use these platforms to consume, or share pirated music, and the devices they use to do so. The researcher interviewed students from various departments at Rajshahi University using a social survey method. According to the study, Rajshahi University students have given up traditional music consumption methods in favour of various digital platforms. In this university, Facebook, YouTube, and Spotify are the most popular platforms for both male and female young students to enjoy music. Music consumption via digital platforms has become an integral part of their daily lives. Students also use social media to share their favorite songs from digital platforms with their family and friends. According to the study, the majority of the students does not listen to music from pirated sources and prefer free sources over digital platforms.

Can Machines Generate Personalized Music? A Hybrid Favorite-Aware Method for User Preference Music Transfer

User preference music transfer (UPMT) is a new problem in music style transfer that can be applied to many scenarios but remains understudied. Transferring an arbitrary song to fit a users preferences increases musical diversity and improves user engagement, which can greatly benefit individuals mental health. Most music style transfer approaches rely on data-driven methods. In general, however, constructing a large training dataset is challenging because users can rarely provide enough of their favorite songs. To address this problem, this paper proposes a novel hybrid method called User Preference Transformer (UP-Transformer) which uses prior knowledge of only one piece of a users favorite music. Based on the distribution of music events in the provided music, we propose a new favorite-aware loss function to fine-tune the Transformer-based model. Two steps are proposed in the transfer phase to achieve UPMT based on the extracted music pattern in a users favorite music. Additionally, to alleviate the problem of evaluating melodic similarity in music style transfer, we propose a new concept called pattern similarity (PS) to measure the similarity between two pieces of music. Statistical tests indicate that the results of PS are consistent with the similarity score in a qualitative experiment. Furthermore, experimental results on subjects show that the transferred music achieves better performance in musicality, similarity, and user preferences.

Lacking autism-linked gene, female birds tune out favorite songs

Lacking autism-linked gene, female birds tune out favorite songs