CANCION ESTILO PROGRESSIVE HOUSE / DEEP HOUSE A 120 BPM.

CANCION ESTILO NEW AGE MEDITACION CON VOCALES FEMENINOS A 100 BPM.

CANCION ESTILO NEW AGE MEDITACION CON VOCALES FEMENINOS A 110 BPM.

CANCION ESTILO NEW AGE MEDITACION INSTRUMENTAL A 110 BPM.

CANCION ESTILO NEW AGE MEDITACION INSTRUMENTAL A 128 BPM.

CANCION ESTILO SYNTHWAVE CON VOCALES MASCULINOS A 100 BPM.

CANCION ESTILO ITALO DISCO CON VOCALES FEMENINOS A 120 BPM.

CANCION ESTILO SYNTHWAVE CON VOCALES FEMENINOS A 90 BPM.

CANCION ESTILO SYNTHWAVE CON VOCALES FEMENINOS A 67.5 BP

CANCION ESTILO ITALO DISCO CON VOCALES FEMENINOS A 120 BPM.

"Falling Stars" is an emotionally charged rap rock and cinematic rock track built on atmospheric, space-themed textures and a powerful rhythmic foundation. Deep 808 bass, live dynamic drums, and heavy drop-tuned guitars create a dense and immersive sound, while ethereal pads expand the track’s cosmic and apocalyptic atmosphere. The song explores themes of collapse, uncertainty, and existential tension, using celestial imagery to reflect inner and collective breakdown. The contrast between rhythmic verses and expansive sections reinforces a sense of descent and urgency. A soaring electric guitar solo acts as a central emotional release, elevating the track from introspection to cinematic intensity. Falling Stars stands as a dramatic and expansive piece where heaviness, atmosphere, and emotional depth converge. Genre: Rap Rock / Cinematic Rock / Alternative Metal Credits: Music and lyrics by Sergio García-Ronrás (Electric Widow – Navegantes Sound Music) Rights: © & ℗ 2025 Navegantes Sound Music. All rights reserved.

“The Game You Lost” delivers an intense soul-rock performance built on gritty electric guitars, a deep groove-driven bassline and powerful organ swells that add emotional weight. The raw, raspy male vocals carry a mix of anger, heartbreak and clarity, shaping a sound that feels both personal and cinematic. The lyrics tell a story of betrayal between brothers—loyalty shattered, love stolen and fame turned hollow. It’s an anthem of loss and rediscovery, where broken trust becomes the catalyst for finding truth and strength while the one who chased glory ends up alone with the consequences. Musical genre: Soul-Rock × Gritty Rock × Emotional Rock Duration: 3:41 min Credits: Music and lyrics by Sergio García-Ronrás (Navegantes Sound Music) Rights: © & ℗ 2025 Navegantes Sound

“Odyssey of the Heart” blends soulful rock with a deep, steady groove, raw electric guitar riffs and smooth organ swells that color the track with warmth and grit. The production moves with a persistent pulse, creating a sense of journey, weight and emotional motion. Vocals shift between tender vulnerability and raspy intensity, reflecting the inner conflict at the core of the song. The lyrics depict a wandering soul returning to places haunted by love—storms weathered, maps broken, anchors dropped with no rest. It’s a voyage driven by memory, longing and the search for meaning in the remains of what once burned bright. Musical genre: Soul Rock × Deep Groove Rock × Blues-Infused Rock Duration: 2:49 min Credits: Music and lyrics by Sergio García-Ronrás (Navegantes Sound Music) Rights: © & ℗ 2025 Navegantes Sound

Melodic progressive house track with a deep, emotional and cinematic atmosphere, driven by lush pads, deep basslines and hypnotic four-on-the-floor drums. The lyrics tell the story of a “complicated mind” walking under the full moon, searching for truth and meaning through empty streets and ancient books, until hidden answers bring inner clarity, healing and a new vision of life.

Original audio recording of the song Deep in the Soul, created using the author’s own lyrics and artistic direction. The instrumental arrangement and vocal rendering were generated with the assistance of Suno AI as a production tool, under the creative supervision of Lorena Lorysa Loraan. This work is available for commercial licensing, including exclusive and non-exclusive rights (music, lyrics, full song). For licensing inquiries, collaboration proposals, or purchase offers, please contact: lorysaloraan@op.pl

Ambient Zen, Meditación Profunda, Música Expansiva. Afinación 432 Hz · Tempo 45 BPM (muy lento). Base: drone sostenido, grave y cálido. Texturas base: pads etéreos espaciales y tonos de “polvo de estrellas”. Estado de ánimo: calma absoluta, inmersión en el universo.

Ambient Zen, Meditación Profunda, Música Expansiva. Afinación 432 Hz · Tempo 45 BPM (muy lento). Base: drone sostenido, grave y cálido. Texturas base: pads etéreos espaciales y tonos de “polvo de estrellas”. Instrumentación: campanas cristalinas, piano suave. Estado de ánimo: calma absoluta, inmersión en el universo. Estructura: inicio con drone → campanas → recursos de piano diminuendo. Instrumental puro.

método basado en deep learning para la clasificación de latidos maternos y fetales a partir de señales aECG de embarazadas, orientado a su implementación en hardware y compatible con entornos de edge computing. En particular, la invención utiliza un autoencoder convolucional unidimensional (1-D Convolutional Autoencoder) que permite detectar la presencia de latidos cardíacos en tiempo real sin necesidad de etapas previas de preprocesamiento para eliminar el ruido. IPR-1192

Tema downtempo con influencias de deep house, house old-school, dance pop y synthpop. La base rítmica mantiene un tempo estable de 120 BPM, con piano arpegiado, bajo sólido y batería marcada de bombo y caja. Los pads de sintetizador crean un ambiente envolvente y atmosférico, mientras la voz femenina principal aporta un tono melancólico pero a la vez esperanzador. La producción es clara y equilibrada, destacando la expresividad vocal y transmitiendo una sensación de superación personal.

We propose a novel approach for American Sign Language (ASL) recognition that combines Google MediaPipe's real-time hand and body landmark tracking with a lightweight deep learning model trained on the MS-ASL dataset. We called this model “Key Frame MLP”, and it extracts the key frames features from the sequence of hand and pose landmarks, enabling efficient recognition without requiring RGB input. Evaluated on the MS-ASL 1000 dataset, our approach achieves 61% top-1 average class accuracy, demonstrating strong performance relative to its simplicity. The entire system is optimized for real-time operation and low computational cost, making it suitable for deployment on edge devices. These results highlight the effectiveness of combining modular MLPs with fast landmark-based inputs for scalable sign language recognition.