Similar Songs Research Articles

EFFECT OF BALINESE FLUTE INSTRUMENT FOR IMPROVING COGNITIVE FUNCTION IN ELDERLY

Background: Cognitive impairment is mostly affected the elderly population which reduces the quality of life for sufferers and their families. The last research showed that listening to Balinese flute instrument music increased cognitive function, especially in the memory domain. However, the study is still limited to small populations. Objective: to determine whether playing the Balinese flute with or without listening to the "Morning Happiness" song affects the cognitive function of elderly. Methods: This experimental study was conducted on healthy geriatric patients above 55 years old with controlled risk factors or no history of any chronic conditions which were divided into 2 groups. The "Morning Happiness" song will be played with Balinese flute instrument in both groups whereas the intervention will add to listen to similar songs. Results: 28 subjects in the intervention group and 20 subjects in the control group. Both groups were comprised of typical gender distribution with an average of 65.08±6.41 years old. Both the intervention and control groups had improved in MoCA-Ina (The Indonesian Version of Montreal Cognitive Assessment) scores, but the intervention group gained a better Z-score (-3.193, p-value 0.001) than the control group (-1.466, p-value 0.143) after procedural. The delayed recall domain also showed significant improvement in both groups with a Z-score of -3.241 (p-value 0.001) in control and -2.30 (p-value 0.026) in intervention group respectively. Interestingly, the attention domain is improved in the control group with a Z-score of -2.142 (p-value 0.032) rather than in intervention group. Conclusion: The overall MoCa-Ina score was significantly higher in interventional subjects than control, specifically in the delayed recall domain.

Evolution of species recognition when ecology and sexual selection favor signal stasis.

The process of reproductive character displacement involves divergence and/or the narrowing of variance in traits involved in species recognition, driven by interactions between taxa. However, stabilizing sexual selection may favor stasis and species similarity in these same traits if signals are optimized for transmission through the prevailing environment. Further, sexual selection may promote increased variability within species to facilitate individual recognition. Here we ask how the conflicting selection pressures of species recognition and sexual selection are resolved in a genus of Himalayan birds that sing exceptionally similar songs. We experimentally show that small differences in two traits (note shape and peak frequency) are both necessary and sufficient for species recognition. Song frequency shows remarkable clinal variation along the Himalayan elevational gradient, being most divergent where species co-occur, the classic signature of reproductive character displacement. Note shape shows no such clinal variation but varies more between individuals of an allopatric species than it does among individuals within species that co-occur. We argue that the different note shapes experience similar transmission constraints, and differences produced through species interactions spread back through the entire species range. Our results imply that reproductive character displacement is likely to be common.

Male and female red-cheeked cordon bleus sing similar yet individualistic songs.

Birdsong is an excellent system for studying complex vocal signaling in both males and females. Historically, most research in captivity has focused only on male song. This has left a gap in our understanding of the environmental, neuroendocrine, and mechanistic control of female song. Here, we report the overall acoustic features, repertoire, and stereotypy of both male and female Red-Cheeked Cordon Bleus (Uraeginthus bengalus) (RCCBs) songs in the lab. We found few sex differences in the acoustic structure, song repertoire, and song stereotypy of RCCBs. Both sexes had similar song entropy, peak frequency, and duration. Additionally, individuals of both sexes sang only a single song type each and had similar levels of song and syllable stereotypy. However, we did find that female RCCBs had higher song bandwidth but lower syllable repertoires. Finally, and most strikingly, we found highly individualistic songs in RCCBs. Each individual produced a stereotyped and unique song with no birds sharing song types and very few syllable types being shared between birds of either sex. We propose that RCCBs represent a promising species for future investigations of the acoustic sex differences in song in a lab environment, and also for understanding the evolutionary driving forces behind individualistic songs.

Potential social facilitation through song in bird communities

The main function of birdsong is to attract mates and defend territories among conspecifics, but little is known about the potential of this behavior in generating interactions among co-existing bird species that signal at the same time. Different hypotheses have been put forward to explain the mechanisms that govern signaling in crowded acoustic spaces, including competition for acoustic space, interspecific territoriality, environmental filters, and acoustic social facilitation. We tested these hypotheses in a forest bird community by studying the behavioral, acoustic, and ecological characteristics of 13 co-occurring species. We used passive recordings to assess the magnitude and the determinants of heterospecific song co-occurrence (i.e., when different species sing at the same time and place), and playback experiments to quantify the propensity of birds to sing if stimulated by heterospecific songs. Birds more frequently sang with species with dissimilar songs. In the breeding season, they also actively sang after the playback of other species, especially if these had dissimilar songs, and occasionally after non-avian unfamiliar playbacks. Heterospecific songs may trigger song utterance because they indicate a predator-free environment, or reduce predation risk through dilution effects, and these benefits may be maximized if there is little acoustic similarity. In line with ecological theory on community assembly mechanisms, these results are compatible with limiting similarity (species with similar songs sing at different times) and suggests social facilitation between phenotypically dissimilar species. These potential biotic interactions through songs may represent a mechanism that leads to bird species coexistence and are worth exploring at the community level.Significance statementIt is generally assumed that bird species avoid acoustic overlap with birds singing similar songs, but there is also evidence of active interspecific territoriality through song displays between pairs of species. Community-wide responses through songs have never been investigated in birds, and to fill this knowledge gap we performed both observations and experiments on multiple coexisting species. Birds tended to sing with species with more dissimilar songs and they were also actively stimulated to sing when the songs of species with dissimilar songs were played back. An interaction between species with dissimilar songs therefore emerged, potentially yielding anti-predatory benefits. Such active behavior, which would have gone unnoticed if observations only were performed, involves multiple species, and therefore may represent a community-wide mechanism of species coexistence.

Response of forest Turtur doves to conspecific and congeneric songs in sympatry and allopatry

Birds have a diverse acoustic communication system, and the ability to recognise their own species’ song from a distance facilitates complex behaviours related to mate attraction and rival deterrence. However, certain species, including doves, do not learn songs and their vocal repertoires are much simpler than those of better-studied songbirds. In these so-called non-learning birds, relatively little is known about the role that bird song plays in intra- and interspecific interactions, and how such behaviours might be acquired (inherited or learned from experience). To investigate this question, we focused on two species of African wood doves whose long-range songs are used in a territorial context. Specifically, we examined the responses of sympatric and allopatric populations of male blue-headed wood-doves (Turtur brehmeri) and tambourine doves (Turtur tympanistria) to different types of simulated territorial intrusions, i.e. playback of conspecific, congeneric, and control songs. We aimed to assess (i) whether these species, which have similar songs, respond only to their own species' song or exhibit interspecific territoriality, and (ii) if the response pattern is affected by the presence or absence of congeners in the general area. We found that both species responded strongly to playback of their own species in both sympatric and allopatric populations. In allopatry, though, male tambourine doves misdirected their response and also approached the playback of congeneric songs. Our results indicate that, in areas where the studied Turtur doves live in sympatry, they do not exhibit consistent interspecific territoriality. However, we cannot exclude the possibility that the smaller tambourine dove avoids its larger congener during the process of territory establishment. The difference in tambourine doves’ response toward the song of present (sympatric) or absent (allopatric) congeners suggests that the ability to discriminate between songs of similarly singing potential competitors is acquired through earlier interactions and learning. This plasticity in response supports the misdirected aggression hypothesis, which argues that interspecific territorialism emerges as a maladaptive by-product of signal similarity. However, on an evolutionary timescale, such an ability could be considered an adaptive cognitive tool useful for resolving competing interests with congeners.

Allopatric montane wren-babblers exhibit similar song notes but divergent vocal sequences

Allopatric montane wren-babblers exhibit similar song notes but divergent vocal sequences

The Transition from Song to Poetry in Latvian Literature in the Second Half of the 19th Century

The Latvian nation is a singing nation. The Singing Revolution and similar song- and singing-related references are traditionally associated with the image of Latvians. The origins of written Latvian are also related to song: the oldest known Christian song in Latvian, dating from 1530, is also one of the oldest examples of Latvian-language text.
 In the second half of the 18th century, as a result of transferring from a German to a Latvian cultural space, a new genre of song was created by the German pastor Gotthard Friedrich Stender, the secular, didactic and sentimental ziņģe (a term coined from the German verb singen, ‘to sing’). As the level of education among Latvians was low and most information spread by word of mouth, the German pastors and the first generation of Latvian poets made use of ziņges as an informative tool. Around the middle of the 19th century, the Latvian national revival began. Without denying the importance of Latvian folk songs in the creation of national culture, the main focus of this article is on the secular ziņģes that were adopted from the German literary tradition, which up until the second half of the 19th century were a favourite tool for entertainment (singing) and spreading information orally. As the level of literacy among Latvians rose, the ziņģes receded to the periphery of the literary landscape, paving the way for a new concept for denoting a rhythmic text that was for the first time not melody-bound: dzeja, or ‘poetry’. The emergence of the new concept in 1869, which was related to the Latvian national revival and the formation of a national literary culture, at the same time also marked a turning point from orality to literacy in Latvian society.

MUSIC RECOMMENDATION SYSTEM BASED ON COSINE SIMILARITY AND SUPERVISED GENRE CLASSIFICATION

Categorizing musical styles can be useful in solving various practical problems, such as establishing musical relationships between songs, similar songs, and finding communities that share an interest in a particular genre. Our goal in this research is to determine the most effective machine learning technique to accurately predict song genres using the K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) algorithms. In addition, this article offers a contrastive examination of the K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) when dimensioning is considered and without using Principal Component Analysis (PCA) for dimension reduction. MFCC is used to collect data from datasets. In addition, each track uses the MFCC feature. The results reveal that the K-Nearest Neighbors and Support Vector Machine offer more precise results without reducing dimensions than PCA results. The accuracy of using the PCA method is 58% and has the potential to decrease. In this music genre classification, K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) are proven to be more efficient classifiers. K-Nearest Neighbors accuracy is 64,9%, and Support Vector Machine (SVM) accuracy is 77%. Not only that, but we also created a recommender system using cosine similarity to provide recommendations for songs that have relatively the same genre. From one sample of the songs tested, five songs were obtained that had the same genre with an average accuracy of 80%.

Character displacement of egg colors during tinamou speciation.

The divergence of reproductive traits frequently underpins the evolution of reproductive isolation. Here we investigated whether tinamou (Tinamidae) egg colorations function as mating signals that diverged as character displacement (mating signal character displacement hypothesis). We tested three evolutionary predictions behind the hypothesis: (a) egg colors coevolve with known mating signals; (b) signal divergence is associated with divergent habitat adaptation; and (c) sympatric tinamou species with similar songs have different egg colors as character displacement during speciation. We found support for all three predictions. In particular, egg colors coevolved with songs; songs and egg colors coevolved with habitat partitioning; and tinamou species that were likely sympatric with similar songs tended to have different egg colors. In conclusion, the mating signal character displacement hypothesis is well supported in which egg colors serve as mating signals that undergo character displacement during tinamou speciation.

Guess who? Evaluating individual acoustic monitoring for males and females of the Tawny Pipit, a migratory passerine bird with a simple song

Individual acoustic monitoring (IAM), based on the analysis of vocal cues, is particularly suitable for the identification and tracking of birds with temporally stable song or call characteristics. Unlike mark-recapture methods, IAM does not require the physical manipulation of individuals, which can have long-lasting behavioural effects. So far, IAM has usually focused on males, as singing females tend to be overlooked in temperate zones. Here, we evaluated the suitability of IAM for both sexes in an isolated population of the Tawny Pipit (Anthus campestris, Motacillidae), a migratory Palearctic species critically endangered in Central Europe, for which female singing has been occasionally documented. We confirmed that songs of all 101 studied individuals, both males and females, were individually distinct. Most individuals used only a single song type in their repertoires, with only three males using two. Of 45 ringed males (that could be unambiguously recognized visually), only two changed their song structure to some extent, either within or between seasons. Multiple individuals often sang structurally similar song types, which nevertheless consistently differed in minor characteristics; such differences were detectable by visual inspection and also affected quantitative analyses of song similarity. Songs sung by females did not have any apparent sex-specific characteristics. Unlike previously suggested, females did not adapt their vocalization to their breeding partner, and we presume their song is also temporally stable. Our findings support IAM as a reliable approach for studying the behaviour and ecology of this passerine species with a small repertoire and simple songs.

Music Information Retrieval Using Similarity Based Relevance Ranking Techniques

The purpose of this proposed study activity is to construct a system for the job of automatically assessing the relevance of music datasets, which will be used in future work. Determine item similarity is an important job in a recommender system since it determines if two items are similar. Participants' systems must provide a list of suggested music that may be added to a given playlist based on a set of playlist characteristics, {which will work along with the algorithms designed to provide other similar songs. Specifically, in this study, the challenges of detecting music similarity only on the basis of song information and tags given by users have been addressed. The proposed technique has been tested using a variety of machine learning algorithms to see how well it performs. tf-idf} and Word2Vec are the methods used to model the dataset and generate feature vectors. It has also been found we that machine learning techniques, including Collaborative Filtering, KNN, Frequent Pattern Growth, and Matrix Factorization, have a greater influence on relevance ranking than traditional methods.

Cambodian American Listening as Memory Work

Cambodian American Listening as Memory Work

Song functions for joint territory defence and within-pair communication in female and male lovely fairy-wrens

Song has been extensively studied in birds and is considered a classic example of a sexually selected trait. However, this interpretation is based predominantly on studies of males. There is growing evidence that female song is common and phylogenetically widespread, but there are still relatively few species for which song similarities and differences between the sexes have been described. We investigated the function of female and male song in the lovely fairy-wren, Malurus amabilis , a tropical species that maintains territories year-round. We tested whether song structure and sex-specific seasonal variation in song rates across breeding and nonbreeding stages were consistent with expectations of key hypotheses for song function. We did this with temporarily caged and free-living birds. We also experimentally examined female and male responses to simulated territorial intrusion, using playbacks simulating solo intruders of either sex singing songs that varied in complexity (i.e. the variety of elements in song). Females and males had similar song structure, complexity and natural song rates and both sang more during nonbreeding than breeding periods. Within each breeding stage, males sang more than females during incubation, a period of female-only investment in parental care. Both sexes sang more when they were more than 5 m apart than when close together in their dense tropical habitat. Females and males responded similarly to simulated intrusion and coordinated their responses. They responded more strongly to male than female song playbacks and to simple playback songs after they were exposed to complex songs, during both breeding and nonbreeding periods. Our observational and experimental data show that female and male songs are similar and suggest that convergent selective pressures may explain similar functions of song, primarily in joint territorial defence of resources and within-pair communication for coordination. • More systematic studies comparing female and male birdsong are needed. • We investigated birds' song form and function in a female and male tropical bird. • Females and males have similar songs which suggests convergent selective pressures. • Both sexes sang more during nonbreeding periods than during breeding periods. • Both sexes have similar song functions for territorial defence and pair communication.

Large-Scale Music Genre Analysis and Classification Using Machine Learning with Apache Spark

The trend for listening to music online has greatly increased over the past decade due to the number of online musical tracks. The large music databases of music libraries that are provided by online music content distribution vendors make music streaming and downloading services more accessible to the end-user. It is essential to classify similar types of songs with an appropriate tag or index (genre) to present similar songs in a convenient way to the end-user. As the trend of online music listening continues to increase, developing multiple machine learning models to classify music genres has become a main area of research. In this research paper, a popular music dataset GTZAN which contains ten music genres is analysed to study various types of music features and audio signals. Multiple scalable machine learning algorithms supported by Apache Spark, including naïve Bayes, decision tree, logistic regression, and random forest, are investigated for the classification of music genres. The performance of these classifiers is compared, and the random forest performs as the best classifier for the classification of music genres. Apache Spark is used in this paper to reduce the computation time for machine learning predictions with no computational cost, as it focuses on parallel computation. The present work also demonstrates that the perfect combination of Apache Spark and machine learning algorithms reduces the scalability problem of the computation of machine learning predictions. Moreover, different hyperparameters of the random forest classifier are optimized to increase the performance efficiency of the classifier in the domain of music genre classification. The experimental outcome shows that the developed random forest classifier can establish a high level of performance accuracy, especially for the mislabelled, distorted GTZAN dataset. This classifier has outperformed other machine learning classifiers supported by Apache Spark in the present work. The random forest classifier manages to achieve 90% accuracy for music genre classification compared to other work in the same domain.

Music Recommendation Using Deep Learning: A Study

Abstract: In this project we suggest songs to a user based on his personal favorite music by analyzing their composition. We take advantage of the Convolutions Neural Network (CNN) to learn meaningful audio explanations and suggest similar songs to the user. To suggest a song for an individual user by his preferences. We have studied some deep learning model which will improve the recommendation accuracy over traditional methods like Collaborative Filtering(CF), Convolutional Neural Network(CNN), Recurrent Neural Network (RNN). In which CNN showed promising results and we have decided to use CNN for our personalized music recommender. Keywords: Deep Learning, Machine Learning, Convolutional Neural Network, Music Recommendation, CNN, Song Recommendation

The Humpback’s New Songs: Diverse and Convergent Evidence Against Vocal Culture via Copying in Humpback Whales

Singing humpback whales constantly modify their songs over hours, days, months, and years, throughout their adult lives. Intriguingly, humpbacks appear to vary songs in concert, with most singers in a population producing similar songs at any given time. The convergent vocal dynamics of singing humpbacks have convinced many that songs are vocal customs, passed from singer to singer through vocal imitation. This interpretation has recently been challenged, however, by the discovery that singers not in acoustic contact may sing highly similar songs, and also appear to change their songs along similar trajectories. How could singers that cannot hear each other culturally conform? Here, it is argued that the changes humpback whales make to songs are inconsistent with either communal copying or competitive improvisation. Instead, singers apar to be continuously morphing the acoustic properties of songs in predictable ways both within and across songs, even in the absence of cross-population interactions. There is no direct evidence that singing humpback whales learn songs by copying other singers. The fact that groups of singers change songs in similar ways is not evidence of vocal imitation, cultural transmission, or cultural evolution. So called “cultural revolutions” in humpback whale songs, which have been touted as the clearest and most impressive evidence of culture in any nonhuman animal, actually provide evidence against vocal culture in humpback whales. Vocal complexity and convergence can arise through mechanisms other than cultural transmission via vocal imitation, and in the case of humpback whales, genetic predispositions and ecological conditions may be more relevant to determining how singers collectively change songs over time.

Intercultural learning through Chinese-American telecollaboration: results of a song sharing project

This study reports on the results of a semester-long Chinese-American telecollaborative exchange via WeChat, in which students from China and the U.S. shared thematically similar songs in their respective languages and discussed relevant cultural topics that were suggested by the students. Various types of qualitative data collected from the exchange (i.e. WeChat group discussion transcripts, videoconferencing audio recordings, final reflection journals, and end-of-semester interviews) showed that the students participated actively in the program and were able to engage in in-depth conversations on various topics with their partners through WeChat group discussion and one-on-one videoconferencing. Many students attributed their positive experience to the intrinsic ability of music to engage college students. In order to evaluate the students’ intercultural learning, Byram’s intercultural competence assessment model was adopted to guide the data analysis. Results showed that the students demonstrated evidence of developing four types of intercultural competences during the exchange: interest in knowing other people’s way of life and introducing one’s own culture to others, knowledge about one’s own and others’ culture for intercultural communication, ability to change perspective, and knowledge about the intercultural communication process, with particularly overwhelming evidence indicating the prominence of the first two types.

An Approach to Discover Similar Musical Patterns

An algorithm has been developed to find the similarity between given songs. The song pattern similarity has been determined by knowing the note structures and the fundamental frequencies of each note of the two songs, under consideration. The statistical concept namely Correlation of Coefficient is used in this work. Correlation of Coefficient is determined by applying 16 Note-Measure Method. If Correlation of Coefficient is near to 1, it indicates that the patterns of the two songs under consideration are similar. Otherwise, there exists a certain percentage of similarity only. This basic principle is used in a set of Indian Classical Music (ICM) based songs. The proposed algorithm can determine the similarity between songs, so that alternative songs in place of some well-known songs can be identified, in terms of the embedded raga patterns. A digital music library has been constructed as a part of this work. The library consists of different songs, their raga name, and their corresponding healing capabilities in terms of music therapy. The proposed work may find application in the area of music therapy. Music therapy is an area of research which is explored significantly in recent time. This work can also be exploited for developing intelligent multimedia tool that is applicable in healthcare domain. A multimedia based mobile app has been developed encapsulating the above mentioned idea that can recommend alternative or similar songs to the existing ICM based songs. This mobile app based music recommendation system may be used for different purposes including entertainment and healthcare. As a result of the applications of the proposed algorithm, similar songs in terms of raga patterns can be discovered from within the pool of a set of songs. A music recommendation system built on this algorithm can retrieve an alternative song from within the pool of songs as a replacement to a well-known song, which otherwise may be used for a particular music therapy. Results are reported and analyzed thoroughly. Future scope of the work is outlined.

Music genre detection using deep learning models

An automated music classification system is that automates the entire process of music classification, i.e., classifying music without any human intervention. Along with this, it is very important to have a good recommendation system that will help with features such as music classification. This paper proposes an automated music classification system that will be very useful for both informational and entertainment purposes in the field of music. This system, based on a musicrelated Artificial Intelligence (AI) algorithm, automatically categorizes different types of music corresponding to different music genres, e.g., Hip Hop, Jazz, Rock, Blues, etc. Another feature added to this system is to recommend similar songs classified by the system. The architecture of the system and the algorithms used at each stage are described and implemented in this paper. The system also provides a lyrics classification module that generates and provides users with lyrics of the user choice. Compared to realistic musical classification, which has always been a difficult problem as it may lack structure or rationality.

Spectrogram cross‐correlation can be used to measure the complexity of bird vocalizations

Abstract Birdsong is an important signal in mate attraction and territorial defence. Quantifying the complexity of these songs can shed light on individual fitness, sexual selection and behaviour. Several techniques have been used to quantify song complexity and be broadly categorized into measures of sequential variations and measures of diversity. However, these methods are unable to account for important acoustic features like the frequency bandwidth and the variety in the spectro‐temporal shape of notes which are an integral part of these vocal signals. This study proposes a new complexity method that considers intra‐song note variability and calculates a weighted index for birdsongs using spectral cross‐correlation. We first compared previously described methods to understand their advantages and limitations based on the factors that would affect the complexity of songs. We then developed a new method, Note Variability Index (NVI), which incorporates the spectral features of notes to quantify complexity. This method alleviates the need for manual classification of notes that can be error‐prone. We used spectrogram cross‐correlation to compare notes within a song and used the output values to quantify song complexity. To evaluate the efficacy of the new method, we generated synthetic songs to caricature extremes in song complexity and compared selected conventional complexity methods along with the NVI. We provide case‐specific limitations of these methods. Additionally, to examine the efficacy of this new method in real‐world scenarios, we used natural birdsongs from multiple species across the globe with varying song structures to compare conventional methods with NVI. To our knowledge, NVI is the only song complexity method that captures the variation of spectro‐temporal shapes of the notes in songs where the conventional methods fail to distinguish between similar song structures with different note types. Furthermore, as NVI does not need a manual classification of notes, it can be easily implemented for any type of birdsong with existing sound analysis software; it is quick, avoids the possible subjectivity in note classification and can be automated for large datasets.