In modern digital world, music plays a role far beyond entertainment—it supports emotional expression, mental well-being, and social connection. However, with countless tracks on streaming platforms, listeners often struggle to find songs that fit their mood. This work proposes a mood-aware Music Recommendation System that uses machine learning and audio signal analysis to deliver personalized suggestions. With Random Forest classifiers, songs are categorized into moods like happiness, sadness, anger, fear, and neutral, and its performance is evaluated not only via accuracy, but also precision, recall, and F1 score, along with confusion matrices to analyze class-by-class prediction quality and achieved 88

This work investigates the electroluminescent properties of Schottky junction LEDs based on nanostructured silicon. Despite the challenges with low efficiency, silicon-based light sources are gaining attention due to their compatibility with optoelectronic integrated circuits. This study explores the effect of metal contacts (Silver, Gold, Copper) that play an influencing role in the electroluminescence properties of these devices, by the key parameters such as the optical transparency, work function and solid solubility of the metals in the silicon oxide matrix. These factors affect injection and extraction efficiencies, as well as the emission spectrum, which is characterized by bias-dependent multiple peaks owing to the radiative transitions between the quantum-confined Bloch states and oxide-related interface trap states within the band gap of the nanostructured silicon. Among the metal contacts studied, Silver provides the highest external quantum efficiency integrated over a broad emission spectrum, demonstrating its potential as a source in photonic devices.

The primary objective of this investigation is to delve into the efficacy of the Genetic Algorithm (GA) in achieving optimal performance when tasked with identifying an ideal assortment of parent rhythms for the generation of offspring music rhythms. The process of selection, a pivotal genetic operation within the reproductive phase of the GA, holds the key to choosing the more adept individuals within the population. These selected individuals then serve as the progenitors of new offspring that will form the subsequent generation.

In the era of vast digital music libraries, the need for automated song categorization based on emotion has grown substantially for personalized recommendations and efficient content organization. This study introduces a novel approach to this problem by leveraging Support Vector Machines (SVMs) to classify songs into predefined emotional categories. By utilizing a meticulously annotated dataset comprising audio features extracted from songs, we address the challenges posed by the subjective nature of musical emotions. Through rigorous experimentation with various SVM kernels, hyperparameters, and feature engineering techniques, our approach achieves high accuracy in predicting the emotional states of songs, demonstrating its efficacy in enhancing music recommendation systems and mood-based music exploration. This research not only advances the field of music information retrieval but also sheds light on opportunities and future directions in music emotion analysis.

In this paper, we conduct a thorough comparison of Genetic Algorithm (GA) performance in the context of parent music rhythm selection to generate diverse offspring music rhythms. We explore two distinct techniques, Tournament Selection and Linear Rank Selection, analyzing key concepts such as Selection Intensity, Loss of Diversity, Selection Variance, Time Complexity, and Takeover time within the domain of Offspring Music Rhythm generation. Our primary objective is to provide a comprehensive assessment of these mechanisms based on their respective properties, aiming to establish a unified perspective on music rhythm parent selection. Additionally, we develop comparison measure software to facilitate this evaluation process and further insights into effective selection strategies for evolving musical compositions.

Silicon nanostructures have been prepared on Si wafer using electrochemical etching process. The transformation of aluminum/nanostructured Si junction from Schottky to Ohmic nature has been observed by varying the annealing temperature. This phenomenon has been explained by the temperature dependent shifting of energy levels of defects states instrumental in trapping charges within the forbidden band gap of Si nanostructures. The Aluminum (Al)/nano-Si junction shows asymmetric nature at room temperature and then changes to ohmic at moderate annealing temperature. Finally, at high temperature the junction becomes rectifying in nature. This transition has been explained on the basis of Fermi level pinning due to the modification of distribution of the non-stoichiometric silicon oxide related defect states present at the nanocrystalline Si core-oxide shell interface.

Negative differential resistance (NDR) has been observed in I-V characteristics measured between two aluminum (Al) pads deposited on a layer containing Silicon nanostructures. This feature has been observed for suitable bias range and specific direction of voltage sweep. NDR has been found to show up within a specific range of lower and upper threshold voltages for each of the samples studied in this work. The amount of NDR has been found to depend on voltage scan rate and bias range. The observed phenomena have been explained using the dynamics of charge trapping and detrapping at the surface/interface defect states present at the boundary of the nanostructured silicon and the oxide layer. An equivalent circuit designed by incorporation of suitable resistance and capacitance representing the trap assisted charge transport within the aluminum-Silicon nanostructure junctions has produced similar I-V characteristics as obtained in the experimental results. Repeatability of NDR shows the potential of the device to be used in oscillators.

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.

Photo-enhanced hysteretic I – V curves have been observed under reverse bias in a p-i-n structure containing electrochemically etched nanostructured silicon (Si) sandwiched between p-Si and n-type a-Si:H layers. These curves have been found to depend on intensity of incident illumination and structural morphology of the nanostructured Si layer. The conductance in trace path is lower than that in retrace path. Charge transport mechanism in this structure has been interpreted using microscopic description of charge trapping and detrapping in the defect states present at the interface of nanocrystalline silicon core and oxide shell in the active layer. An applied voltage dependent probability distribution of trapping and detrapping has been calculated in light of classical random walk problem. The trapping/detrapping of charges leading to development/destruction of potential barriers in the path of charge flow shows an analogy with the river bed deposition/erosion. The rate of trapping has been considered to depend on the empty defect states whereas the rate of detrapping depends on the already filled defects. Moreover, the rate of both trapping and detrapping is expected to depend on the charge flow rate. All these considerations lead the I – V relations for trace and retrace paths in reverse bias fitting nicely with experimental I – V loops. The observed peaks in the voltage dependent dynamic conductance in trace and retrace paths have been explained as a consequence of development and destruction of two barriers in the active layer for electrons and holes separately. Best fit values of the fitting parameters indicates that the trace path is dominated by holes whereas the retrace path is dominated by electronic transport. The difference in mobility of electron and hole leads to different trapping and detrapping rates in the two paths resulting in the observed hysteresis.

Musical rhythmic structures are the perfect combination of beats that are the most inseparable components of music, containing the musical notes. The knowledge sharing on music rhythm patterns and their applications for generating improvised musical performance is a challenging task for the novices of computational musicology. Memetic computing can be very effective in finding near-optimum solutions in the context of rhythmic pattern modeling. We have illustrated the tournament selection strategy to construct the fruitful rhythmic patterns. We have incorporated the mutation operators to combine the mutation effects on rhythmic structures after multi-point crossover for obtaining optimum prolific musical composition. In this contribution, we have projected a learning framework, which recognizes the elementary music rhythm structures and improvises the rhythm patterns for enhancing the excellence of source rhythm patterns by the memetic algorithm. The tournament selection mechanism has been performed for selecting the parent rhythms efficiently to generate rhythmic offspring.

Normally, music recommendation system is the software that is used to create user’s personalized list of music. The paper proposed an approach of music recommendation system based on user’s zodiac sign. According to the date of birth of a person, there must be one specific zodiac sign, and each zodiac sign may have some basic features, like lucky number, suitable color, suitable stone, behavior, habit, and so on. According to the Indian classical music, there are 72 Melakarta or parent ragas and these ragas are subdivided into 12 chakras or cycles, and each cycle consists of six individual ragas. All the cycles have also some specific features like, color, stone, position, etc. In this work, the key feature is the stone. So after finding the suitable stone of one user, it has been implementing the key feature and mapping the required raga cycle. Any song from that raga cycle is suitable for that person; therefore, the work offers to create the song list generation of one specific raga cycle which contains six ragas.

The paper proposes an intelligent method to generate similarities among songs by finding the similarities of fundamental frequencies using the F-Test tool. The primary objective of this tool is to find whether two or more independent series of fundamental frequencies of the highest occurrence notes of song music variance differ significantly or whether two song samples may be regarded as the same variance. If the calculated value of F-Test is compared with the table value for the degree of freedom for a sample song having larger variance and the degree of freedom for sample song having smaller variance at 5% and 1% level of significance, then the hypothesis is accepted. Hence, it may be regarded that two song frequency patterns have the same variance and their fundamental frequency patterns are similar.

Music has two fundamental elements like the rhythm and melody. Rhythm is a perfect arrangement of notes that is the most inseparable component of music containing the length of note structures in a musical composition. Knowledge sharing on rhythmic patterns and their applications for creating new Music is a difficult task for learners of musicology. The memetic algorithm has been shown to be very effective in finding near-optimum solutions to the modeling of rhythmic patterns. The tournament selection strategy of evolutionary algorithms has been used to construct the fruitful rhythmic patterns for music composition. The work has been introduced to identify the parent rhythms and then create the versatile offspring rhythms using memetic algorithm. The tournament selection mechanism has been initiated for parent rhythms selection process for generating offspring rhythmic structures.

The paper proposes a method to determine the complexity of a particular song. A song is one of the units that consists of a set of the music elements, which is sung by human and it comprises structures of different note patterns. In the context of Indian music, songs may vary with their style, genre, complexity, melody, etc. Therefore, song complexity is one of the quality metric factors that determine the character of the song. Hence, determination of song complexity of a particular song is an important task. This work presents an application using some statistical measures that can determine the complexity of a song. The ultimate goal of this work is to explore the complexity part of songs, which is one of the quality metric factors of a song and it also has great impact in the field of musicology.

A device, supporting negative differential resistance, has been fabricated. Aluminum top contacts have been deposited on electrochemically synthesized porous silicon layer on p-type silicon substrate for this purpose. Top-bottom DC current vs. voltage (I-V) characteristics of the fabricated device have been recorded at room temperature under dark condition and injecting photons. Dark I-V characteristics of this device indicate the presence of negative differential resistance (NDR). Similar kind of I-V characteristics has been observed under illumination. At the same time, fabricated device shows photovoltaic effect after injecting photons. This may be due to some series and parallel combination of multiple junctions with photovoltaic nature.

The paper presents an intelligent method to generate list of songs online for listening, download according to age factor of users. Context-aware is a process used in smartphone or computer system that can sense their physical environment and adopt their behavior correspondingly. It is a Web-based application software that recommended the different songs depends upon the listener choice based on their age group from the music library and also classify the unknown songs in the same cluster depends on the review of user.

All type of music pieces consist of two vital elements -rhythm structure and melody involvement.Another most important thing for automatic music improvisation is cadences that give symphonic shape to a music melody.Intelligent quality music composition is the relation of different musical elements and aggregation entity of these musical element objects.This paper introduces a new method for composing music using abstraction mechanism concept of software engineering.This paper focuses on the two main objects of music -Vocal and Instrumental and these two objects are tightly coupled and create the aggregation entity of Music Composition.The primary objective of this work is to explore the efficiency of Abstraction mechanism -Generalization, Specialization, and Aggregation to search for an optimum combination between vocal and different instrumentals with their different tempo to improve the quality of music and versatile intelligent music composition.

A p-i-n heterostructure containing electrochemically synthesized silicon (Si) nanorods embedded in a nonstoichiometric silicon oxide matrix sandwiched as i-layer between p-Si and n-type hydrogeneted amorphous Si shows hysteresis in both forward and reverse biases with an additional switching in forward bias. Conductivity in the trace path is lesser than the retrace path. Hysteresis in the reverse bias has been found to get enhanced up to three orders of magnitude under illumination by laser sources of different intensities and wavelengths showing the potential of the structure as an effective memory device. Hysteresis area and conductivity become maximum for red light and gradually decrease for green and violet light for fixed intensity. It is well known that the Si nanocrystal–silicon oxide interface contains a lot of electron and hole trap levels within the bandgap. Trapping and detrapping of photogenerated carriers at the trap/defect states are expected to affect the band bending at the junctions. The observed optically enhanced hysteresis has been explained through formation and destruction of the potential barrier at junctions during trace and retrace paths, respectively. The potential has been estimated by solving Poisson's equation, and the current–voltage (I–V) relation for trace and retrace paths has been derived where the rate of trapping and detrapping becomes different resulting in the observed hysteresis. Theoretically obtained I–V characteristics match well with the experimentally obtained results. The trap density in the i-layer estimated to be ∼1011/cm2 is in good agreement for the trap density in similar systems.